didier-durand · June 25, 2025 03:34
diff --git a/mcp-sse-trafic-Strands-Agent-Atlassian-MCP-server.txt b/mcp-sse-trafic-Strands-Agent-Atlassian-MCP-server.txt
 Request event hook: GET - url: https://mcp.atlassian.com/v1/sse - content: b'' - headers: Headers({'host': 'mcp.atlassian.com', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'accept': 'text/event-stream', 'cache-control': 'no-store'}) - Waiting for response
 Response event hook: GET https://mcp.atlassian.com/v1/sse - Status: 200
  response chunk 1: b'event: endpoint\ndata: /v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece\n\n'
 Request event hook: POST - url: https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - content: b'{"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"mcp","version":"0.1.0"}},"jsonrpc":"2.0","id":0}' - headers: Headers({'host': 'mcp.atlassian.com', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'content-length': '152', 'content-type': 'application/json'}) - Waiting for response
  response chunk 2: b'event: message\ndata: {"jsonrpc":"2.0","id":0,"result":{"protocolVersion":"2025-03-26","capabilities":{"tools":{"listChanged":true}},"serverInfo":{"name":"Atlassian MCP","version":"1.0.0"}}}\n\n'
 Response event hook: POST https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - Status: 202
  response chunk 1: b'Accepted'
 Request event hook: POST - url: https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - content: b'{"method":"notifications/initialized","jsonrpc":"2.0"}' - headers: Headers({'host': 'mcp.atlassian.com', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'content-length': '54', 'content-type': 'application/json'}) - Waiting for response
 Response event hook: POST https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - Status: 202
  response chunk 1: b'Accepted'
 Request event hook: POST - url: https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - content: b'{"method":"tools/list","jsonrpc":"2.0","id":1}' - headers: Headers({'host': 'mcp.atlassian.com', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'content-length': '46', 'content-type': 'application/json'}) - Waiting for response
 Response event hook: POST https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - Status: 202
  response chunk 1: b'Accepted'
  response chunk 3: b'event: message\ndata: {"jsonrpc":"2.0","id":1,"result":{"tools":[{"name":"atlassianUserInfo","description":"Get current user info from Atlassian","inputSchema":{"type":"object","properties":{},"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getAccessibleAtlassianResources","description":"Get cloudid to construct API calls to Atlassian REST APIs","inputSchema":{"type":"object","properties":{},"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getConfluenceSpaces","description":"Get spaces from Confluence","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"ids":{"anyOf":[{"type":"string"},{"type":"array","items":{"type":"number"}}]},"keys":{"anyOf":[{"type":"string"},{"type":"array","items":{"type":"string"}}]},"type":{"type":"string","enum":["global","collaboration","knowledge_base","personal"]},"status":{"type":"string","enum":["current","archived"]},"labels":{"anyOf":[{"type":"string"},{"type":"array","items":{"type":"string"}}]},"favoritedBy":{"type":"string"},"notFavoritedBy":{"type":"string"},"sort":{"type":"string"},"descriptionFormat":{"type":"string","enum":["plain","view"]},"includeIcon":{"type":"boolean"},"cursor":{"type":"string"},"limit":{"type":"number"}},"required":["cloudId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getConfluencePage","description":"Get a page from Confluence","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string"}},"required":["cloudId","pageId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getPagesInConfluenceSpace","description":"Get pages in a Confluence space","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"spaceId":{"type":"string"},"limit":{"type":"number","description":"Maximum number of pages to return (default: 25, max: 250)"},"cursor":{"type":"string","description":"Opaque cursor for pagination"},"status":{"type":"string","enum":["current","archived","deleted","trashed"],"description":"Filter pages by status"},"title":{"type":"string","description":"Filter pages by title"},"sort":{"type":"string","enum":["id","-id","created-date","-created-date","modified-date","-modified-date","title","-title"],"description":"Sort pages by field(s)"},"depth":{"type":"string","enum":["all","root"],"description":"Filter pages by depth, default: all"}},"required":["cloudId","spaceId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getConfluencePageAncestors","description":"Get all ancestors of a Confluence page","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string"},"limit":{"type":"number"}},"required":["cloudId","pageId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getConfluencePageFooterComments","description":"Get footer comments for a Confluence page","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string"},"limit":{"type":"number"},"cursor":{"type":"string"},"status":{"type":"string","enum":["current","archived","trashed","deleted","historical","draft"],"default":"current"},"sort":{"type":"string","enum":["id","-id","created-date","-created-date"]}},"required":["cloudId","pageId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getConfluencePageInlineComments","description":"Get inline comments for a Confluence page","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string"},"limit":{"type":"number"},"cursor":{"type":"string"},"status":{"type":"string","enum":["current","archived","trashed","deleted","historical","draft"],"default":"current"},"resolutionStatus":{"type":"string","enum":["resolved","open","dangling","reopened"],"default":"open"},"sort":{"type":"string","enum":["id","-id","created-date","-created-date"]}},"required":["cloudId","pageId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getConfluencePageDescendants","description":"Get descenda'
  response chunk 4: b'nts of a Confluence page","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string"},"limit":{"type":"number"},"depth":{"type":"number"},"cursor":{"type":"string"}},"required":["cloudId","pageId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"createConfluencePage","description":"Create a new page in Confluence","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"spaceId":{"type":"string"},"title":{"type":"string"},"parentId":{"type":"string"},"body":{"type":"string","description":"The content of the page. You **MUST** use markdown format."},"isPrivate":{"type":"boolean","description":"The page will be private. Only the user who creates this page will have permission to view and edit one."}},"required":["cloudId","spaceId","body"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"updateConfluencePage","description":"Update an existing page in Confluence","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string"},"title":{"type":"string"},"status":{"type":"string","enum":["current","draft"]},"spaceId":{"type":"string"},"parentId":{"type":"string"},"body":{"type":"string","description":"The content of the page. You **MUST** use markdown format."},"versionNumber":{"type":"number"},"versionMessage":{"type":"string"}},"required":["cloudId","pageId","title","status","body","versionNumber"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"createConfluenceFooterComment","description":"Create a footer comment on a Confluence page or blog post","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string","description":"The id of the page to add the comment to."},"parentCommentId":{"type":"string","description":"The id of the parent comment to reply to."},"attachmentId":{"type":"string","description":"The id of the attachment to add to the comment."},"customContentId":{"type":"string","description":"The id of the custom content to add to the comment."},"body":{"type":"string","description":"The content of the comment in Markdown format."}},"required":["cloudId","body"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"createConfluenceInlineComment","description":"Create an inline comment on a page or blog post","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"pageId":{"type":"string","description":"The id of the page to add the comment to."},"parentCommentId":{"type":"string","description":"The id of the parent comment to reply to."},"body":{"type":"string","description":"The content of the comment in Markdown format."},"inlineCommentProperties":{"type":"object","properties":{"textSelection":{"type":"string","description":"The text to highlight"},"textSelectionMatchCount":{"type":"number","description":"The number of matches for the selected text on the page (should be strictly greater than textSelectionMatchIndex)"},"textSelectionMatchIndex":{"type":"number","description":"The match index to highlight. This is zero-based. E.g. if you have 3 occurrences of \\"hello world\\" on a page and you want to highlight the second occurrence, you should pass 1 for textSelectionMatchIndex and 3 for textSelectionMatchCount."}},"required":["textSelection","textSelectionMatchCount","textSelectionMatchIndex"],"additionalProperties":false,"description":"Object describing the text to highlight on the page/blog post. Only applicable for top level inline comments (not replies) and required in that case."}},"required":["cloudId","body"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"searchConfluenceUsingCql","description":"Search content in Confluence using CQL","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"cql":{"type":"string"},"cqlcontext":{"type":"string"},"cursor":{"type":"string"},"expand":{"type":"string"},"limit":{"type":"number"},"prev":{"type":"boolean"},"next":{"type":"boolean"}},"required":["cloudId","cql"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getJiraIssue","description":"Get the details of a Jira issue by issue id or key.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"issueIdOrKey":{"type":"string","description":"Issue id or key can be used to uniquely identify an existing issue.\\nIssue id is a numerical identifier. An example issue id is 10000.\\nIssue key is formatted as a project key followed by a hyphen \'-\' character and then followed by a sequential number.\\nAn example issue key is ISSUE-1."},"fields":{"type":"array","items":{"type":"string"}},"fieldsByKeys":{"type":"boolean"},"expand":{"type":"string"},"properties":{"type":"array","items":{"type":"string"}},"updateHistory":{"type":"boolean"},"failFast":{"type":"boolean"}},"required":["cloudId","issueIdOrKey"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"editJiraIssue","description":"Update the details of an existing Jira issue id or key.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"issueIdOrKey":{"type":"string","description":"Issue id or key can be used to uniquely identify an existing issue.\\nIssue id is a numerical identifier. An example issue id is 10000.\\nIssue key is formatted as a project key followed by a hyphen \'-\' character and then followed by a sequential number.\\nAn example issue key is ISSUE-1."},"fields":{"type":"object","additionalProperties":{}}},"required":["cloudId","issueIdOrKey","fields"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"createJiraIssue","description":"Create a new Jira issue in a given project with a given issue type.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"projectKey":{"type":"string","description":"A project key in Jira is a unique identifier (a string of letters, numbers and sometimes underscores) of a project.\\n      There is a tool \\"getVisibleJiraProjects\\" to look up which projects the user has create permission to create a new Jira issue."},"issueTypeName":{"type":"string","description":"A Jira issue type categorizes and  distinguishes different kinds of work being tracked within a project.\\n      It\'s a way to define what a specific piece of work represents.\\n      For example, in a Jira Software project, there are \\"Epic\\", \\"Story\\", \\"Task\\", \\"Bug\\" or \\"Subtask\\" issue types by default.\\n      Or in Jira Service Management, there are \\"Change\\", \\"IT help\\", \\"Incident\\", \\"New feature\\", \\"Problem\\", \\"Service request\\", \\"Service request with approval\\" or \\"Support\\" issue types by default.\\n      User can remove those default ones and/or define their own issue types as well.\\n      There is a tool \\"getJiraProjectIssueTypesMetadata\\" to get the available issue types in a given project."},"summary":{"type":"string"},"description":{"type":"string","description":"The content of the issue\'s description in Markdown format."},"assignee_account_id":{"type":"string","description":"During issue creation, we can set an assignee for the new issue.\\n      The input must be an account id of a valid user in the given cloud id.\\n      There is a tool \\"atlassianUserInfo\\" to get the account id of the current user.\\n      There is a tool \\"lookupJiraAccountId\\" to get the account ids of the existing users in Jira based on the user\'s display name or email address."},"additional_fields":{"type":"object","additionalProperties":{}}},"required":["cloudId","projectKey","issueTypeName","summary"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getTransitionsForJiraIssue","description":"Get available transitions for an existing Jira issue id or key.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"issueIdOrKey":{"type":"string","description":"Issue id or key can be used to uniquely identify an existing issue.\\nIssue id is a numerical identifier. An example issue id is 10000.\\nIssue key is formatted as a project key followed by a hyphen \'-\' character and then followed by a sequential number.\\nAn example issue key is ISSUE-1."},"expand":{"type":"string"},"transitionId":{"type":"string"},"skipRemoteOnlyCondition":{"type":"boolean"},"includeUnavailableTransitions":{"type":"boolean"},"sortByOpsBarAndStatus":{"type":"boolean"}},"required":["cloudId","issueIdOrKey"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"transitionJiraIssue","description":"Transition an existing Jira issue (that has issue id or key) to a new status.","inputSchema":{"type":"object","proper'
  response chunk 5: b'ties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"issueIdOrKey":{"type":"string","description":"Issue id or key can be used to uniquely identify an existing issue.\\nIssue id is a numerical identifier. An example issue id is 10000.\\nIssue key is formatted as a project key followed by a hyphen \'-\' character and then followed by a sequential number.\\nAn example issue key is ISSUE-1."},"transition":{"type":"object","properties":{"id":{"type":"string"}},"required":["id"],"additionalProperties":false},"fields":{"type":"object","additionalProperties":{}},"update":{"type":"object","additionalProperties":{"type":"array","items":{"type":"object","additionalProperties":{"description":"This is the field value. The actual value will depends on the field type."}},"description":"List of operations"}},"historyMetadata":{"type":"object","properties":{"type":{"type":"string"},"description":{"type":"string"},"descriptionKey":{"type":"string"},"activityDescription":{"type":"string"},"activityDescriptionKey":{"type":"string"},"emailDescription":{"type":"string"},"emailDescriptionKey":{"type":"string"},"actor":{"type":"object","properties":{"id":{"type":"string"},"displayName":{"type":"string"},"type":{"type":"string"},"avatarUrl":{"type":"string"},"url":{"type":"string"}},"additionalProperties":false},"generator":{"type":"object","properties":{"id":{"type":"string"},"displayName":{"type":"string"},"type":{"type":"string"},"avatarUrl":{"type":"string"},"url":{"type":"string"}},"additionalProperties":false},"cause":{"type":"object","properties":{"id":{"type":"string"},"displayName":{"type":"string"},"type":{"type":"string"},"avatarUrl":{"type":"string"},"url":{"type":"string"}},"additionalProperties":false},"extraData":{"type":"object","additionalProperties":{"type":"string"}}},"additionalProperties":false}},"required":["cloudId","issueIdOrKey","transition"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"lookupJiraAccountId","description":"Lookup account ids of existing users in Jira based on the user\'s display name or email address.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"searchString":{"type":"string"}},"required":["cloudId","searchString"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"searchJiraIssuesUsingJql","descrip'
  response chunk 6: b'tion":"Search Jira issues using Jira Query Language (JQL).","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"jql":{"type":"string","description":"A Jira Query Language (JQL) expression to search Jira issues"},"maxResults":{"type":"number","maximum":100,"default":50,"description":"A maximum number of issue to search per page. Default is 50, max is 100"},"fields":{"type":"array","items":{"type":"string"},"default":["summary","description","status","issuetype","priority","created"]},"nextPageToken":{"type":"string","description":"This is used for pagination purpose to fetch more data if a JQL search has more issues in next pages"}},"required":["cloudId","jql"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"addCommentToJiraIssue","description":"Adds a comment to an existing Jira issue id or key.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"issueIdOrKey":{"type":"string","description":"Issue id or key can be used to uniquely identify an existing issue.\\nIssue id is a numerical identifier. An example issue id is 10000.\\nIssue key is formatted as a project key followed by a hyphen \'-\' character and then followed by a sequential number.\\nAn example issue key is ISSUE-1."},"commentBody":{"type":"string","description":"The content of the comment in Markdown format."},"commentVisibility":{"type":"object","properties":{"type":{"type":"string","enum":["group","role"],"description":"Whether visibility of this comment is restricted to a group or role."},"value":{"type":"string","description":"The name of the group or role that visibility of this comment is restricted to."}},"required":["type","value"],"additionalProperties":false}},"required":["cloudId","issueIdOrKey","commentBody"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getJiraIssueRemoteIssueLinks","description":"Get remote issue links (eg: Confluence links etc...) of an existing Jira issue id or key","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"issueIdOrKey":{"type":"string","description":"Issue id or key can be used to uniquely identify an existing issue.\\nIssue id is a numerical identifier. An example issue id is 10000.\\nIssue key is formatted as a project key followed by a hyphen \'-\' character and then followed by a sequential number.\\nAn example issue key is ISSUE-1."},"globalId":{"type":"string","description":"An identifier for the remote item in the remote system.\\n          For example, the global ID for a remote item in Confluence would consist of the app ID and page ID, like this: appId=456&pageId=123.\\n          When a global ID is provided, this tool returns only the remote issue link of the given Jira issue that has the provided global ID.\\n          When no global ID is provided, this tool returns all the remote issue links of the given Jira issue."}},"required":["cloudId","issueIdOrKey"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getVisibleJiraProjects","description":"Get visible Jira projects for which the user has either view, browse, edit or create permission on that project.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"searchString":{"type":"string","description":"Filter the results using a literal string. Projects with a matching key or name are returned (case insensitive)"},"action":{"type":"string","enum":["view","browse","edit","create"],"default":"create","description":"\\n      Filter results by projects for which the user can:\\n      * view the project\\n      * browse the project\\n      * edit the project\\n      * create the project, meaning that they have the Create issues project permission for the project in which the issue is created.\\n      "},"startAt":{"type":"number","default":0,"description":"The index of the first item to return in a page of results (page offset)."},"maxResults":{"type":"number","maximum":50,"default":50,"description":"The maximum number of items to return per page."},"expandIssueTypes":{"type":"boolean","default":true,"description":"To include additional information about the issue types associated with the project"}},"required":["cloudId"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}},{"name":"getJiraProjectIssueTypesMetadata","description":"Get a page of issue type metadata for a specified project. The issue type metadata will be used to create issue.","inputSchema":{"type":"object","properties":{"cloudId":{"type":"string","description":"Unique identifier for an Atlassian Cloud instance in the form of a UUID. Can also be a site URL. If not working, use the \'getAccessibleAtlassianResources\' tool to find accessible Cloud IDs."},"projectIdOrKey":{"type":"string"},"startAt":{"type":"number","default":0,"description":"The index of the first item to return in a page of results (page offset)."},"maxResults":{"type":"number","maximum":200,"default":50,"description":"The maximum number of items to return per page."}},"required":["cloudId","projectIdOrKey"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}}]}}\n\n'
 I'll help you create a list of all Jira tickets that are not closed but past their due date. To do this, I need to:

 1. Get access to your Atlassian environment
 2. Search for Jira issues using JQL (Jira Query Language) with the appropriate query
 3. Format the results with the ticket ID, title, and summaries

 Let me start by getting access to your Atlassian resources:
 Tool #1: getAccessibleAtlassianResources
 Request event hook: POST - url: https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - content: b'{"method":"tools/call","params":{"name":"getAccessibleAtlassianResources","arguments":{}},"jsonrpc":"2.0","id":2}' - headers: Headers({'host': 'mcp.atlassian.com', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'content-length': '113', 'content-type': 'application/json'}) - Waiting for response
 Response event hook: POST https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - Status: 202
  response chunk 1: b'Accepted'
  response chunk 7: b'event: message\ndata: {"jsonrpc":"2.0","id":2,"result":{"content":[{"type":"text","text":"[{\\"id\\":\\"ba9a141c-9295-4fd5-ac2f-35ca28f0897c\\",\\"url\\":\\"https://mcp-tests.atlassian.net\\",\\"name\\":\\"mcp-tests\\",\\"scopes\\":[\\"read:jira-work\\",\\"write:jira-work\\"],\\"avatarUrl\\":\\"https://site-admin-avatar-cdn.prod.public.atl-paas.net/avatars/240/pencilmarker.png\\"}]"}]}}\n\n'
 Now I'll search for Jira issues that are not closed but past their due date using JQL. I'll use the query: "duedate < now() AND resolution = Unresolved"
 Tool #2: searchJiraIssuesUsingJql
 Request event hook: POST - url: https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - content: b'{"method":"tools/call","params":{"name":"searchJiraIssuesUsingJql","arguments":{"cloudId":"ba9a141c-9295-4fd5-ac2f-35ca28f0897c","jql":"duedate < now() AND resolution = Unresolved","fields":"summary,description,status,issuetype,priority,created,duedate"}},"jsonrpc":"2.0","id":3}' - headers: Headers({'host': 'mcp.atlassian.com', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'content-length': '279', 'content-type': 'application/json'}) - Waiting for response
  response chunk 8: b'event: message\ndata: {"jsonrpc":"2.0","id":3,"error":{"code":-32602,"message":"MCP error -32602: Invalid arguments for tool searchJiraIssuesUsingJql: [\\n  {\\n    \\"code\\": \\"invalid_type\\",\\n    \\"expected\\": \\"array\\",\\n    \\"received\\": \\"string\\",\\n    \\"path\\": [\\n      \\"fields\\"\\n    ],\\n    \\"message\\": \\"Expected array, received string\\"\\n  }\\n]"}}\n\n'
 Response event hook: POST https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - Status: 202
  response chunk 1: b'Accepted'
 I apologize for the error. Let me correct it and retry with the proper format for fields:
 Tool #3: searchJiraIssuesUsingJql
 Request event hook: POST - url: https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - content: b'{"method":"tools/call","params":{"name":"searchJiraIssuesUsingJql","arguments":{"jql":"duedate < now() AND resolution = Unresolved","fields":["summary","description","status","issuetype","priority","created","duedate"],"cloudId":"ba9a141c-9295-4fd5-ac2f-35ca28f0897c"}},"jsonrpc":"2.0","id":4}' - headers: Headers({'host': 'mcp.atlassian.com', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'python-httpx/0.28.1', 'authorization': '[secure]', 'content-length': '293', 'content-type': 'application/json'}) - Waiting for response
 Response event hook: POST https://mcp.atlassian.com/v1/sse/message?sessionId=a92c511ff7e43700ffd49dd0138e8cf7e903c13ed1cd2f84e5b69ac3a020fece - Status: 202
  response chunk 1: b'Accepted'
  response chunk 9: b'event: message\ndata: {"jsonrpc":"2.0","id":4,"result":{"content":[{"type":"text","text":"{\\"issues\\":[{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10055\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10055\\",\\"key\\":\\"ECS-31\\",\\"fields\\":{\\"summary\\":\\"AI-Driven Fraud Detection System with Real-time Transaction Monitoring\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-12-15\\",\\"created\\":\\"2025-06-22T10:09:06.847+0200\\",\\"description\\":\\"## Overview\\\\n\\\\nThe AI-powered Core Banking system requires an advanced fraud detection capability that leverages machine learning algorithms to identify and flag suspicious transactions in real-time while minimizing false positives.\\\\n\\\\n## Business Need\\\\n\\\\nFinancial institutions using our core banking platform are experiencing increasingly sophisticated fraud attempts. Traditional rule-based systems are becoming less effective against evolving fraud tactics. A more intelligent, adaptive solution is required to protect our clients and their customers.\\\\n\\\\n## Proposed Solution\\\\n\\\\nImplement an AI-driven fraud detection module with the following capabilities:\\\\n\\\\n### Key Features\\\\n\\\\n1. **Real-time Transaction Analysis**\\\\n\\\\n    * Process and analyze transactions as they occur\\\\n    * Provide fraud risk scoring within 100ms of transaction initiation\\\\n    * Support for all transaction types (card, wire, ACH, mobile payments)\\\\n    \\\\n2. **Multi-layered Machine Learning Models**\\\\n\\\\n    * Behavioral analysis to establish customer transaction patterns\\\\n    * Anomaly detection for identifying deviations from normal patterns\\\\n    * Network analysis to identify connections between accounts and transactions\\\\n    * Deep learning models to detect complex fraud patterns\\\\n    \\\\n3. **Contextual Authentication**\\\\n\\\\n    * Step-up authentication triggers based on risk scoring\\\\n    * Integration with biometric verification systems\\\\n    * Location intelligence with geofencing capabilities\\\\n    \\\\n4. **Adaptive Rule Engine**\\\\n\\\\n    * Self-learning system that adjusts rules based on new fraud patterns\\\\n    * Ability for financial institutions to customize rules and thresholds\\\\n    * Regulatory compliance rule templates for different jurisdictions\\\\n    \\\\n5. **Comprehensive Dashboard**\\\\n\\\\n    * Real-time visualization of fraud attempts\\\\n    * Case management system for fraud investigation teams\\\\n    * Audit trails and reporting for regulatory compliance\\\\n    \\\\n\\\\n## Technical Requirements\\\\n\\\\n* Architecture must support processing of 10,000+ transactions per second\\\\n* Latency for fraud scoring must not exceed 100ms\\\\n* 99.99% system uptime requirement\\\\n* Compliance with PCI-DSS, GDPR, and regional banking regulations\\\\n* APIs for integration with existing banking systems and third-party security solutions\\\\n* Ability to operate in both cloud and on-premise environments\\\\n\\\\n## Success Metrics\\\\n\\\\n* Reduction in fraud losses by at least 30%\\\\n* False positive rate below 2%\\\\n* Customer friction reduction by 15% compared to current systems\\\\n* Investigation time for fraud cases reduced by 40%\\\\n\\\\n## Timeline\\\\n\\\\n* Phase 1: Development and internal testing - 3 months\\\\n* Phase 2: Pilot implementation with select clients - 2 months\\\\n* Phase 3: Full rollout and optimization - 2 months\\\\n\\\\n## Additional Considerations\\\\n\\\\n* The solution must be explainable to satisfy regulatory requirements\\\\n* Training data must be anonymized to protect customer privacy\\\\n* Model retraining should be automated based on performance metrics\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10053\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10053\\",\\"key\\":\\"ECS-29\\",\\"fields\\":{\\"summary\\":\\"Critical Bug: Loan Amortization Calculation Error Causing Incorrect Customer Statements\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:44:18.953+0200\\",\\"duedate\\":\\"2024-04-30\\",\\"description\\":\\"## Bug Summary\\\\n\\\\nThere\'s a critical calculation error in the loan amortization module of the AI-powered Core Banking system that is incorrectly calculating the principal-to-interest ratio on adjustable-rate mortgage (ARM) loans. This error is causing incorrect customer statements and potentially impacting thousands of mortgage accounts.\\\\n\\\\n## Impact\\\\n\\\\n* Approximately 3,200 ARM customers are receiving incorrect monthly statements\\\\n* Principal payments are being under-reported by an average of 2.3%\\\\n* Interest payments are being over-reported by a corresponding amount\\\\n* Tax documents (Form 1098) may contain inaccurate mortgage interest information\\\\n* Customer trust is at risk as statements don\'t match actual loan balance progression\\\\n\\\\n## Reproduction Steps\\\\n\\\\n1. Create a test ARM loan with an initial rate of 3.25% and adjustment to 4.75% after 6 months\\\\n2. Process 7 monthly payments (crossing the rate adjustment boundary)\\\\n3. Compare the principal/interest breakdown in the system vs. manual calculation\\\\n4. Note the discrepancy begins exactly after the interest rate adjustment occurs\\\\n\\\\n## Technical Details\\\\n\\\\n* The error appears in the `calculateAdjustableRateAmortization()` method in the `LoanCalculationServiceImpl` class\\\\n* Line 347 uses the previous interest rate when recalculating the amortization schedule after a rate adjustment\\\\n* Debug logs show the rate change is correctly recorded in the database but not properly applied to ongoing calculations\\\\n* The error only manifests with rate increases; rate decreases paradoxically calculate correctly\\\\n\\\\n## Business Rules Being Violated\\\\n\\\\n* Regulation Z (Truth in Lending Act) requires accurate disclosure of loan terms and costs\\\\n* Internal accuracy standards require amortization tables to balance to the penny over the life of the loan\\\\n* Customer monthly statements must accurately reflect the breakdown of payments\\\\n\\\\n## Supporting Evidence\\\\n\\\\n* Screenshots of incorrect statements attached in JIRA\\\\n* SQL query used to identify affected accounts:\\\\n\\\\n```sql\\\\nSELECT account_id, loan_type, orig_rate, current_rate\\\\nFROM loan_accounts\\\\nWHERE loan_type IN (\'ARM5\', \'ARM7\', \'ARM10\')\\\\nAND rate_change_date < CURRENT_DATE\\\\nAND rate_change_date > ADD_MONTHS(CURRENT_DATE, -12);\\\\n```\\\\n\\\\n## Temporary Workaround\\\\n\\\\nThe mortgage servicing team has implemented a manual recalculation process for affected customers who call in with concerns, but this is not scalable to all affected accounts.\\\\n\\\\n## Security Implications\\\\n\\\\nNone directly, but the bug could result in regulatory compliance issues and potential customer litigation.\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10052\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10052\\",\\"key\\":\\"ECS-28\\",\\"fields\\":{\\"summary\\":\\"AI-Powered Real-Time Fraud Detection System for International Wire Transfers\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/res'
  response chunk 10: b't/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2023-12-15\\",\\"created\\":\\"2025-06-22T09:42:33.893+0200\\",\\"description\\":\\"## Overview\\\\n\\\\nOur banking customers are experiencing increased fraudulent activities on international wire transfers. We need to implement an AI-powered real-time fraud detection system that can analyze transfer patterns, detect anomalies, and prevent fraudulent transactions before they are completed.\\\\n\\\\n## Business Context\\\\n\\\\nInternational wire transfers have become a prime target for sophisticated fraud schemes. Current rule-based systems are failing to catch many emerging fraud patterns, resulting in significant financial losses and customer dissatisfaction. The manual review process is time-consuming and often results in false positives that inconvenience legitimate customers.\\\\n\\\\n## Proposed Feature Details\\\\n\\\\n* **Machine Learning Models**: Implement advanced ML models to analyze transaction patterns, beneficiary history, and customer behavior profiles\\\\n* **Real-time Decision Engine**: Create a system capable of making go/no-go decisions within 500ms during the transaction process\\\\n* **Risk Scoring System**: Develop a comprehensive risk scoring mechanism that factors in:\\\\n\\\\n    * Geographic risk factors\\\\n    * Transaction amount relative to customer history\\\\n    * Time and frequency patterns\\\\n    * Beneficiary risk assessment\\\\n    * Device and channel information\\\\n    \\\\n\\\\n## Integration Requirements\\\\n\\\\n* Seamless integration with existing SWIFT and Ripple payment networks\\\\n* Connection to core banking transaction processing system\\\\n* Integration with customer communication systems for immediate notifications\\\\n* Dashboard for compliance team to review flagged transactions\\\\n\\\\n## Success Metrics\\\\n\\\\n* Reduce fraudulent transactions by 85%\\\\n* Maintain false positive rate below 1%\\\\n* No increase in transaction processing time beyond 800ms\\\\n* ROI of at least 500% within first year of implementation\\\\n\\\\n## Technical Considerations\\\\n\\\\n* Model training and retraining pipeline must be established\\\\n* Data privacy and regulatory compliance must be maintained\\\\n* System must operate 24/7/365 with 99.99% uptime\\\\n* API documentation for all integration points required\\\\n\\\\n## Timeline Expectations\\\\n\\\\n* Proof of concept: 60 days\\\\n* Beta implementation: 120 days\\\\n* Full rollout: 180 days\\\\n\\\\n## Additional Requirements\\\\n\\\\n* Comprehensive audit logs for regulatory compliance\\\\n* Customizable rule engine to complement AI decisions\\\\n* Regular reporting on system performance and fraud prevention metrics\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10051\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10051\\",\\"key\\":\\"ECS-27\\",\\"fields\\":{\\"summary\\":\\"Implement Predictive Cash Flow Management for SME Customers\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10004\\",\\"id\\":\\"10004\\",\\"description\\":\\"Stories track functionality or features expressed as user goals.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10315?size=medium\\",\\"name\\":\\"Story\\",\\"subtask\\":false,\\"avatarId\\":10315,\\"entityId\\":\\"c2d2a70b-635e-41e4-b257-a0ae17c29f4d\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:40:34.159+0200\\",\\"duedate\\":\\"2024-10-17\\",\\"description\\":\\"## User Story\\\\n\\\\nAs a small to medium-sized business owner, I want the banking system to provide AI-powered cash flow predictions and management tools so I can better anticipate financial needs, optimize working capital, and avoid liquidity issues.\\\\n\\\\n## Business Context\\\\n\\\\nSmall and medium enterprises frequently face cash flow challenges that can threaten their operations. By leveraging our AI capabilities and the transaction data we already have, we can provide valuable predictive insights and management tools that help these customers maintain healthy cash positions and potentially avoid costly short-term financing.\\\\n\\\\n## Detailed Requirements\\\\n\\\\n### AI Prediction Engine\\\\n\\\\n* Analyze historical transaction patterns to predict future cash inflows and outflows\\\\n* Identify recurring revenue streams and expenses with their probability and timing\\\\n* Detect seasonal patterns in business cash flow\\\\n* Account for outliers and one-time transactions\\\\n* Progressive learning to improve prediction accuracy over time\\\\n* Minimum 85% accuracy on 30-day cash flow predictions\\\\n* Warning indicators for potential cash shortfalls\\\\n* Confidence scores for all predictions\\\\n\\\\n### Cash Flow Dashboard\\\\n\\\\n* Visual representation of current cash position across all accounts\\\\n* 30/60/90 day cash flow projections with confidence intervals\\\\n* Automated categorization of income and expenses\\\\n* Drill-down capability from summary to transaction-level detail\\\\n* Comparison of actual vs. predicted cash flows\\\\n* \\\\\\"What-if\\\\\\" scenario modeling for potential business decisions\\\\n* Custom alerts for user-defined cash thresholds\\\\n* Exportable reports for accounting and planning purposes\\\\n\\\\n### Working Capital Optimization\\\\n\\\\n* Intelligent suggestions for invoice payment timing\\\\n* Early payment discount optimization recommendations\\\\n* Identification of potential late fees based on predicted cash position\\\\n* Automated scheduling of payments to optimize cash position\\\\n* Recommendations for transferring excess cash to higher-yield accounts\\\\n* Identification of opportunities to negotiate better payment terms\\\\n\\\\n### Integration Points\\\\n\\\\n* Connect w'
  response chunk 11: b'ith accounting software (Xero, QuickBooks, etc.)\\\\n* Import invoice and payment data from external systems\\\\n* Integrate with accounts payable/receivable workflows\\\\n* Sync with inventory management systems for stock-related cash predictions\\\\n* API access for custom integrations with business intelligence tools\\\\n\\\\n## Acceptance Criteria\\\\n\\\\n1. System accurately predicts cash position within 15% margin of error for 30-day forecasts\\\\n2. Dashboard loads in under 3 seconds with complete data visualization\\\\n3. Automated categorization achieves at least 90% accuracy for common transaction types\\\\n4. Alert system successfully notifies users of predicted cash shortfalls at least 14 days in advance\\\\n5. What-if scenarios can be created, modified, and compared in real-time\\\\n6. All cash flow data can be exported in common formats (CSV, Excel, PDF)\\\\n7. System makes actionable recommendations that quantify potential cash flow improvements\\\\n\\\\n## Technical Considerations\\\\n\\\\n* Need to establish secure API connections to accounting software providers\\\\n* ML model training requires representative dataset across business types\\\\n* Real-time data processing requirements for immediate transaction reflection\\\\n* Data privacy considerations for sensitive financial information\\\\n* Mobile optimization for on-the-go access\\\\n\\\\n## User Experience Requirements\\\\n\\\\n* Clean, intuitive dashboard design with minimal financial jargon\\\\n* Guided onboarding to establish baseline cash flow patterns\\\\n* Contextual help for interpreting cash flow projections\\\\n* Mobile-responsive design for access on all devices\\\\n* Clear visualization of prediction confidence levels\\\\n\\\\n## Measurable Outcomes\\\\n\\\\n* Reduce cash flow surprises for SME customers by 40%\\\\n* Decrease short-term financing needs by 25% through better planning\\\\n* Increase customer retention for SME accounts by 15%\\\\n* Generate 30% higher engagement with digital banking platform\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10050\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10050\\",\\"key\\":\\"ECS-26\\",\\"fields\\":{\\"summary\\":\\"Inconsistent Mortgage Payment Calculation for Bi-Weekly Payment Schedule\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-08-15\\",\\"created\\":\\"2025-06-22T09:38:12.399+0200\\",\\"description\\":\\"## Bug Description\\\\n\\\\nThe mortgage calculation module in our AI-powered Core Banking system is incorrectly calculating bi-weekly payment amounts for certain mortgage products. The system is applying an incorrect day-count convention for bi-weekly payment schedules, resulting in payment miscalculations. This affects both customer-facing payment calculators and the internal loan servicing system.\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Create a new mortgage with the following parameters:\\\\n\\\\n    * Principal amount: $450,000\\\\n    * Interest rate: 4.25% fixed\\\\n    * Term: 30 years\\\\n    * Payment frequency: Bi-weekly\\\\n    * Product type: Conventional fixed-rate mortgage\\\\n    \\\\n2. Generate amortization schedule and payment information\\\\n3. Verify the calculated bi-weekly payment amount\\\\n\\\\n## Expected Behavior\\\\n\\\\nThe system should calculate the correct bi-weekly payment amount:\\\\n\\\\n* Annual payment divided by 26 (for 26 bi-weekly periods per year)\\\\n* Principal and interest correctly amortized over the loan term\\\\n* Extra payment impact on loan term correctly calculated\\\\n* Payment amount: $1,011.78 per bi-weekly payment (based on standard mortgage formulas)\\\\n\\\\n## Actual Behavior\\\\n\\\\nThe system is incorrectly calculating the bi-weekly payment as $1,045.21, which is approximately 3.3% higher than the correct amount. The amortization schedule also shows incorrect interest allocation throughout the loan term, with a bias toward more interest in the early payments than is mathematically correct.\\\\n\\\\n## Root Cause Analysis (Initial)\\\\n\\\\nThe calculation error appears to be related to how the system is converting between payment frequencies:\\\\n\\\\n1. The system is incorrectly using a simple division of the monthly payment by 2, rather than recalculating the payment amount based on 26 annual payments\\\\n2. The day count convention appears to be using a 30/360 method when it should be using actual/365 for bi-weekly payments\\\\n3. The compounding interest calculation for bi-weekly frequency is not correctly accounting for the higher payment frequency\\\\n\\\\n## Impact\\\\n\\\\n* Approximately 2,300 mortgage accounts with bi-weekly payment schedules are affected\\\\n* Customers are being charged incorrect payment amounts (typically 3-4% higher than correct amount)\\\\n* Amortization schedules provided to customers show incorrect payoff timelines\\\\n* Regulatory compliance risk due to incorrect payment calculations\\\\n* Customer service receiving increased inquiries about payment calculations\\\\n* Potential reputational damage if not addressed\\\\n\\\\n## Environment Details\\\\n\\\\n* Core Banking System Version: 4.2.1\\\\n* Mortgage Module Version: 2.3.5\\\\n* Calculation Engine: FinMath Library v3.2\\\\n* Database: PostgreSQL 14.1\\\\n\\\\n## Affected Code Areas\\\\n\\\\n* `/src/main/java/com/bank/mortgage/calculation/PaymentCalculator.java`\\\\n* `/src/main/java/com/bank/mortgage/service/AmortizationService.java`\\\\n* `/src/main/java/com/bank/mortgage/util/DayCountConvention.java`\\\\n\\\\n## Workaround\\\\n\\\\nCustomer service representatives are currently using an external calculator to verify bi-weekly payment amounts and manually adjusting the payment schedule when customers inquire about discrepancies.\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/3\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/medium_new.svg\\",\\"name\\":\\"Medium\\",\\"id\\":\\"3\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10049\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10049\\",\\"key\\":\\"ECS-25\\",\\"fields\\":{\\"summary\\":\\"Multi-Currency Wallet with AI-Powered FX Optimization\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:35:53.435+0200\\",\\"duedate\\":\\"2024-06-30\\",\\"description\\":\\"## Feature Overview\\\\n\\\\nImplement a multi-currency wallet feature in our AI-powered Core Banking system that allows customers to hold, exchange, and manage multiple currencies within a single account, with AI-powered exchange rate optimization to minimize conversion costs.\\\\n\\\\n## Business Value\\\\n\\\\n* Attract international customers and expatriates who deal with multiple currencies\\\\n* Generate additional revenue from foreign exchange transactions\\\\n* Reduce customer attrition by eliminating the need for multiple bank accounts\\\\n* Competitive advantage in the growing digital nomad and global remote worker segment\\\\n* Cross-selling opportunity for international payment and remittance services\\\\n\\\\n## Detailed Requirements\\\\n\\\\n### Multi-Currency Account Infrastructure\\\\n\\\\n* Support for holding balances in at least 15 major currencies simultaneously\\\\n* Real-time balance display in customer\'s preferred base currency\\\\n* Segregated ledger system to maintain regulatory compliance for multi-currency holdings\\\\n* Support for interest calculation on individual currency balances\\\\n* Integration with global payment networks for each supported currency\\\\n\\\\n### Currency Exchange Capabilities\\\\n\\\\n* Real-time exchange rate display with spread transparency\\\\n* Scheduled currency conversions (one-time or recurring)\\\\n* Rate alerts based on customer-defined thresholds\\\\n* Historical exchange rate charts and analysis tools\\\\n* API integration with multiple liquidity providers for competitive rates\\\\n\\\\n### AI-Powered Optimization Features\\\\n\\\\n* Smart conversion timing recommendations based on historical rate patterns\\\\n* Personalized FX strategy suggestions based on customer\'s transaction history\\\\n* Automated micro-conversions to take advantage of favorable rates\\\\n* Forward contract recommendations for large upcoming currency needs\\\\n* Machine learning models to predict short-term currency movements for customer benefit\\\\n\\\\n### User Experience Requirements\\\\n\\\\n* Intuitive visualization of currency allocations and conversions\\\\n* Clear transaction history for all currency movements\\\\n* Mobile-first design with streamlined conversion workflow\\\\n* Push notifications for rate alerts and conversion opportunities\\\\n* Educational content about currency management integrated into the interface\\\\n\\\\n## Technical Considerations\\\\n\\\\n* Real-time integration with FX market data providers\\\\n* Compliance with international banking regulations for multi-currency accounts\\\\n* Enhanced security measures for international transactions\\\\n* Scalable architecture to support additional currencies in the future\\\\n* Performance requirements for real-time rate calculations and conversions\\\\n\\\\n## Success Metrics\\\\n\\\\n* Customer adoption rate: Target 15% of eligible customers in the first year\\\\n* Foreign exchange revenue: Increase of \xe2\x82\xac2M annually\\\\n* Customer satisfaction: Achieve 4.5/5 rating for the feature\\\\n* Reduction in third-party FX service usage by our customers\\\\n* Increase in international transaction volume by 25%\\\\n\\\\n## Localization Requirements\\\\n\\\\n* Interface available in 10 major languages\\\\n* Culturally appropriate currency display formats\\\\n* Compliance with local financial regulations in target markets\\\\n* Region-specific currency groupings based on customer location\\\\n\\\\n## Rollout Strategy\\\\n\\\\n* Phase 1: Support for USD, EUR, GBP, JPY, CHF\\\\n* Phase 2: Add AUD, CAD, CNY, HKD, SGD\\\\n* Phase 3: Add remaining currencies based on customer demand\\\\n* Initial beta release to international banking customers\\\\n* Gradual rollout to all customer segments\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/4\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/low_new.svg\\",\\"name\\":\\"Low\\",\\"id\\":\\"4\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10048\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10048\\",\\"key\\":\\"ECS-24\\",\\"fields\\":{\\"summary\\":\\"Intermittent Authentication Failure Causing Payment Processing Delays\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-04-20\\",\\"created\\":\\"2025-06-22T09:34:00.536+0200\\",\\"description\\":\\"## Bug Description\\\\n\\\\nThe AI-powered Core Banking system is experiencing intermittent authentication failures during high-volume payment processing periods, resulting in transaction delays and failed payments. The authentication service is sporadically rejecting valid credentials from the payment gateway during peak processing times.\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Monitor system during high transaction volume periods (typically 10:00-11:30 AM and 2:00-3:30 PM)\\\\n2. Observe authentication service logs for increased error rates\\\\n3. Correlation with payment gateway connection pool utilization >80%\\\\n4. Authentication tokens expire prematurely or are invalidated incorrectly\\\\n\\\\n## Expected Behavior\\\\n\\\\nAuthentication service should maintain consistent performance during peak processing times, with proper token validation and renewal, ensuring uninterrupted payment processing.\\\\n\\\\n## Actual Behavior\\\\n\\\\n* Authentication service sporadically returns HTTP 401/403 errors for valid credentials\\\\n* OAuth token validation fails with error code \\\\\\"ERR-AUTH-5291\\\\\\"\\\\n* Connection pool shows \\\\\\"socket hang up\\\\\\" errors\\\\n* Average of 12-15% of payment transactions affected during peak periods\\\\n* Authentication service CPU spikes to 95% utilization\\\\n* Redis cache shows elevated miss rates during token validation\\\\n* Logs show increased latency in token verification microservice (>500ms vs normal <50ms)\\\\n\\\\n## Environment Details\\\\n\\\\n* Authentication Service Version: 2.4.3\\\\n* Payment Gateway Version: 3.1.0\\\\n* Load Balancer: F5 Big-IP v16.1\\\\n* Redis Cache Cluster: v6.2\\\\n* Kubernetes Cluster: v1.23.4\\\\n* Service Mesh: Istio 1.12.3\\\\n\\\\n## Impact\\\\n\\\\n* CRITICAL: Affecting approximately 12-15% of payment transactions during peak times\\\\n* Customer payment delays of 30+ minutes\\\\n* Increased customer support calls (43% increase during affected periods)\\\\n* Risk of duplicate payment submissions by customers retrying transactions\\\\n* Potential compliance issues with payment processing SLAs\\\\n* Settlement delays with partner banks\\\\n\\\\n## Diagnostic Information\\\\n\\\\n* Error frequency increases exponentially when transaction volume exceeds 5,000 TPS\\\\n* Authentication service logs show increased connection timeouts to the identity provider\\\\n* Memory usage in authentication pods stable at 70-75%\\\\n* No recent deployments or configuration changes to the authentication service\\\\n* Issue began approximately 3 days ago after network infrastructure maintenance\\\\n\\\\n## Workaround\\\\n\\\\nCurrently manually increasing authentication service replica count during peak times and forcing token refresh every 10 minutes instead of the standard 60 minutes.\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/ji'
  response chunk 12: b'ra/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10047\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10047\\",\\"key\\":\\"ECS-23\\",\\"fields\\":{\\"summary\\":\\"Implement Personalized Financial Insights Dashboard for Retail Customers\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10004\\",\\"id\\":\\"10004\\",\\"description\\":\\"Stories track functionality or features expressed as user goals.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10315?size=medium\\",\\"name\\":\\"Story\\",\\"subtask\\":false,\\"avatarId\\":10315,\\"entityId\\":\\"c2d2a70b-635e-41e4-b257-a0ae17c29f4d\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:32:24.731+0200\\",\\"duedate\\":\\"2024-02-15\\",\\"description\\":\\"## User Story\\\\n\\\\nAs a retail banking customer, I want to access a personalized financial insights dashboard that analyzes my spending patterns, savings behavior, and financial goals, so that I can make better financial decisions and improve my financial health.\\\\n\\\\n## Background\\\\n\\\\nOur AI-powered Core Banking system collects vast amounts of data about customer transactions, but currently provides limited analytical insights back to customers. By leveraging AI to analyze transaction data, we can provide personalized financial advice and insights that improve customer engagement and satisfaction.\\\\n\\\\n## Detailed Requirements\\\\n\\\\n### Data Analysis Requirements\\\\n\\\\n* Categorize transactions automatically (groceries, utilities, entertainment, etc.)\\\\n* Identify recurring payments and subscriptions\\\\n* Calculate monthly spending averages by category\\\\n* Detect unusual spending patterns or transactions\\\\n* Analyze income stability and cash flow patterns\\\\n* Predict future account balance based on historical patterns\\\\n\\\\n### User Interface Requirements\\\\n\\\\n* Clean, intuitive dashboard with key financial metrics\\\\n* Visual representations of spending by category (charts/graphs)\\\\n* Spending trends over time (weekly, monthly, yearly comparisons)\\\\n* Goal progress trackers for saving objectives\\\\n* Customizable date ranges for analysis\\\\n* Mobile and desktop responsive design\\\\n\\\\n### Recommendations Engine Requirements\\\\n\\\\n* Suggest potential savings based on spending habits\\\\n* Identify forgotten or unused subscriptions\\\\n* Provide tips for optimizing credit usage\\\\n* Recommend appropriate banking products based on financial behavior\\\\n* Alert users to potential cashflow issues before they occur\\\\n\\\\n### User Customization\\\\n\\\\n* Ability to correct transaction categorization\\\\n* Option to set budgets by category\\\\n* Capability to create and track financial goals\\\\n* Preferences for notification frequency and types\\\\n\\\\n## Acceptance Criteria\\\\n\\\\n1. Dashboard successfully displays at least 6 months of historical transaction data\\\\n2. Transaction categorization has at least 85% accuracy\\\\n3. UI renders correctly on both mobile and desktop browsers\\\\n4. Users can create, track, and modify financial goals\\\\n5. System generates at least 3 personalized insights per month for active users\\\\n6. Dashboard load time is under 2 seconds for 95% of users\\\\n\\\\n## Technical Considerations\\\\n\\\\n* Must comply with data privacy regulations\\\\n* Integration with existing authentication system\\\\n* API connection to the transaction processing system\\\\n* Caching strategy for performance optimization\\\\n* Analytics to measure feature usage and effectiveness\\\\n\\\\n## Additional Information\\\\n\\\\n* Design mockups are available in Figma (link)\\\\n* Customer research indicating desired features (link)\\\\n* Technical architecture document (link)\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/3\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/medium_new.svg\\",\\"name\\":\\"Medium\\",\\"id\\":\\"3\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10046\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10046\\",\\"key\\":\\"ECS-22\\",\\"fields\\":{\\"summary\\":\\"System Timeout During Batch Loan Processing for Large Corporate Clients\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:31:05.707+0200\\",\\"duedate\\":\\"2023-12-10\\",\\"description\\":\\"## Bug Description\\\\n\\\\nDuring overnight batch processing of corporate loans, the system times out when processing portfolios with more than 500 active loans, causing incomplete interest calculations and affecting next-day reporting.\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Initiate end-of-day batch processing for corporate loan accounts\\\\n2. Include a corporate client with 500+ active loans (e.g., ClientID: CORP-78923)\\\\n3. Monitor processing logs during loan interest calculation phase\\\\n4. Observe system timeout after approximately 12 minutes of processing\\\\n\\\\n## Expected Behavior\\\\n\\\\nThe system should successfully complete the batch processing of all loans regardless of portfolio size, with proper interest calculations and reporting generation.\\\\n\\\\n## Actual Behavior\\\\n\\\\n* System logs show timeout errors after 12 minutes\\\\n* Process terminates with error code ERR-3092\\\\n* Batch job marked as \\\\\\"Incomplete\\\\\\"\\\\n* Interest calculations are missing for loans that weren\'t processed\\\\n* Next-day reports show incorrect outstanding balances\\\\n\\\\n## Environment Details\\\\n\\\\n* Version: AI-powered Core Banking v3.2.4\\\\n* Database: Oracle 19c Enterprise\\\\n* Server configuration: 16 cores, 64GB RAM\\\\n* Batch processing window: 11:00 PM to 4:00 AM\\\\n* Average loan portfolio size affected: 700+ loans\\\\n\\\\n## Impact\\\\n\\\\n* Critical financial reporting errors for large corporate clients\\\\n* Manual intervention required to complete calculations\\\\n* Risk of regulatory non-compliance due to incorrect interest calculations\\\\n* Customer statements potentially delayed\\\\n\\\\n## Workaround\\\\n\\\\nCurrently, we manually split the processing of large portfolios into multiple smaller batches, but this requires overnight staff and delays the completion of the batch cycle.\\\\n\\\\n## Attachments\\\\n\\\\n* System logs showing timeout (attached)\\\\n* Sample error report (attached)\\\\n* Performance monitoring snapshot (attached)\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10045\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10045\\",\\"key\\":\\"ECS-21\\",\\"fields\\":{\\"summary\\":\\"AI-Powered Fraud Detection for Real-time Transaction Monitoring\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:29:46.232+0200\\",\\"duedate\\":\\"2023-10-15\\",\\"description\\":\\"## Background\\\\n\\\\nOur AI-powered Core Banking system needs advanced fraud detection capabilities to protect customers and the bank from increasingly sophisticated fraud attempts.\\\\n\\\\n## Proposed Feature\\\\n\\\\nImplement an AI-driven fraud detection module that can:\\\\n\\\\n* Analyze transactions in real-time using machine learning algorithms\\\\n* Create dynamic customer behavior profiles to establish transaction patterns\\\\n* Identify anomalies based on geolocation, transaction size, beneficiary history, and time patterns\\\\n* Adjust risk thresholds automatically based on learned patterns\\\\n* Generate real-time alerts for suspicious activities with risk scoring\\\\n* Provide a case management interface for fraud analysts\\\\n* Integrate with existing AML/KYC systems\\\\n\\\\n## Technical Requirements\\\\n\\\\n* Must process transactions with latency under 100ms\\\\n* Support for both rule-based and ML-based detection algorithms\\\\n* API integration with third-party data sources for identity verification\\\\n* Fully auditable transaction verification path\\\\n* Configurable alert thresholds by product type and customer segment\\\\n* Dashboard for fraud pattern visualization and reporting\\\\n\\\\n## Success Metrics\\\\n\\\\n* Reduce false positives by 40% compared to current system\\\\n* Increase fraud detection rate by at least 25%\\\\n* Maintain 99.99% system availability\\\\n\\\\n## Due Date\\\\n\\\\nOctober 15, 2023\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10044\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10044\\",\\"key\\":\\"ECS-20\\",\\"fields\\":{\\"summary\\":\\"Bug: Customer Data Synchronization Failures Between Mobile App and Core System\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2025-05-20\\",\\"created\\":\\"2025-06-22T09:17:57.820+0200\\",\\"description\\":\\"## Bug Description\\\\n\\\\nCustomer profile updates made through the mobile banking app are intermittently failing to synchronize with the core banking system. This results in inconsistent customer information across different banking channels and potentially impacts service delivery.\\\\n\\\\n## Symptoms\\\\n\\\\n1. Customer address updates via mobile app not reflecting in branch banking systems\\\\n2. Phone number and email changes not propagating to notification systems\\\\n3. KYC document updates not reflecting in compliance monitoring systems\\\\n4. Contact preference changes not being honored for marketing communications\\\\n\\\\n## Impact\\\\n\\\\n* Customer frustration when repeatedly asked for information they\'ve already provided\\\\n* Potential compliance issues with KYC/AML regulations\\\\n* Failed communications due to outdated contact information\\\\n* Increased call center volume (estimated 12% of current support calls)\\\\n* Trust degradation in our digital banking services\\\\n\\\\n## Technical Analysis\\\\n\\\\n* Logs show successful API calls from the mobile app to the API gateway\\\\n* The synchronization issue occurs in the data integration layer between the API gateway and the core banking system\\\\n* Error logs show intermittent timeouts (Error code: SYNC_TIMEOUT_5023)\\\\n* Successful updates trigger the appropriate Kafka events, but consumer acknowledgment is inconsistent\\\\n* Dead letter queue shows messages with malformed payloads in approximately 8% of cases\\\\n* Issue frequency increases during peak usage periods (8-10 AM and 5-7 PM local time)\\\\n\\\\n## Reproduction Steps\\\\n\\\\n1. Log in to the mobile banking app (v4.2.3 or later)\\\\n2. Navigate to profile settings and update any personal information field\\\\n3. Observe \\\\\\"Success\\\\\\" message in the app\\\\n4. Check core banking system (CBS) or other channels within 30 minutes\\\\n5. Approximately 15-20% of updates will not be reflected in CBS\\\\n\\\\n## Environment Details\\\\n\\\\n* Mobile App versions: 4.2.3 - 4.3.1 (iOS and Android)\\\\n* API Gateway: Kong 2.8.1\\\\n* Integration Layer: Spring Integration 5.5.3\\\\n* Message Broker: Kafka 2.8.0\\\\n* Core Banking System: AI-powered Core Banking v3.1.4\\\\n\\\\n## Diagnostic Information\\\\n\\\\n* Error logs attached as error_logs_cust_sync_20250501.zip\\\\n* Database transaction logs show successful writes to the mobile app database\\\\n* Integration middleware logs show message transformation success but delivery confirmation failures\\\\n* Network latency between API gateway and core banking system shows periodic spikes\\\\n\\\\n## Business Context\\\\n\\\\nThis issue affects approximately 15-20% of all customer data updates, impacting around 2,500 customers per week. The operations team is currently implementing manual data reconciliation as a workaround, which requires 2-3 FTEs.\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10043\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/1004'
  response chunk 13: b'3\\",\\"key\\":\\"ECS-19\\",\\"fields\\":{\\"summary\\":\\"Develop ESG Investment Portfolio Analysis and Recommendation Engine\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2025-03-15\\",\\"created\\":\\"2025-06-22T09:14:39.406+0200\\",\\"description\\":\\"## Feature Overview\\\\n\\\\nDevelop a comprehensive Environmental, Social, and Governance (ESG) investment portfolio analysis and recommendation engine for our AI-powered Core Banking platform. This feature will enable our customers to make sustainable investment decisions aligned with their personal values while maintaining financial performance objectives.\\\\n\\\\n## Business Justification\\\\n\\\\n* The global ESG investment market is expected to reach $53 trillion by 2025, representing more than one-third of projected total assets under management\\\\n* 76% of our millennial customers and 69% of Gen-Z customers have expressed interest in ESG investing options\\\\n* Regulatory trends across major markets are increasingly requiring ESG disclosures for financial products\\\\n* Competitors have begun offering basic ESG screening, creating a competitive disadvantage for our platform\\\\n\\\\n## Functional Requirements\\\\n\\\\n### ESG Data Integration\\\\n\\\\n* Integrate with multiple ESG data providers (MSCI, Sustainalytics, S&P Global, Bloomberg)\\\\n* Develop a unified ESG scoring methodology that normalizes ratings across providers\\\\n* Create real-time data pipelines for continuous updates of ESG metrics\\\\n* Implement data quality monitoring and anomaly detection\\\\n\\\\n### Portfolio Analysis Features\\\\n\\\\n* Comprehensive ESG portfolio scoring across E, S, and G dimensions\\\\n* Carbon footprint calculation and comparison to benchmarks\\\\n* Exposure analysis to controversial sectors (fossil fuels, weapons, tobacco, etc.)\\\\n* UN Sustainable Development Goals (SDG) alignment assessment\\\\n* Greenwashing detection algorithms using natural language processing\\\\n\\\\n### Recommendation Engine\\\\n\\\\n* AI-powered ESG investment recommendations based on client preferences\\\\n* Impact vs. return optimization algorithms\\\\n* Personalized ESG goal setting with progress tracking\\\\n* Portfolio rebalancing suggestions to improve ESG scores while maintaining return targets\\\\n* Tax-efficient ESG transition strategies for existing portfolios\\\\n\\\\n### User Experience\\\\n\\\\n* Interactive ESG dashboard with customizable views\\\\n* Visualization of ESG impact in both financial and real-world terms\\\\n* Comparison tools for different investment options\\\\n* ESG education resources tailored to user knowledge level\\\\n* Social sharing capabilities for impact achievements\\\\n\\\\n## Technical Requirements\\\\n\\\\n* Scalable data processing pipeline handling 100+ million data points daily\\\\n* Real-time scoring capabilities for on-demand portfolio analysis\\\\n* Machine learning models for ESG trend prediction and anomaly detection\\\\n* API integration with third-party ESG data providers\\\\n* Compliance with data privacy regulations (GDPR, CCPA)\\\\n* Comprehensive audit trail for regulatory compliance\\\\n\\\\n## Integration Points\\\\n\\\\n* Core banking investment platform\\\\n* Customer preference database\\\\n* Financial planning tools\\\\n* Reporting and document generation systems\\\\n* Mobile and web applications\\\\n* CRM system for advisor insights\\\\n\\\\n## Success Criteria\\\\n\\\\n* Increase ESG investment adoption by 40% within 6 months of launch\\\\n* Customer satisfaction scores for ESG features exceeding 85%\\\\n* 30% reduction in time required for ESG portfolio analysis by advisors\\\\n* 25% increase in average investment amount for ESG portfolios vs. traditional offerings\\\\n* Regulatory compliance with all major ESG disclosure requirements\\\\n\\\\n## Implementation Timeline\\\\n\\\\nThis is a strategic initiative with a target completion date of March 15, 2025.\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/3\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/medium_new.svg\\",\\"name\\":\\"Medium\\",\\"id\\":\\"3\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\'
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The APR displayed to customers is incorrectly calculated by up to 0.3% compared to the actual rate that should be applied based on regulatory guidelines.\\\\n\\\\n## Impact\\\\n\\\\n* Potential regulatory compliance issues with Truth in Lending Act (TILA) and Regulation Z\\\\n* Customer confusion and complaints about rate discrepancies\\\\n* Risk of legal liability for inaccurate loan disclosures\\\\n* Reputational damage as a trusted financial institution\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Create a new mortgage application with the following parameters:\\\\n\\\\n    * Loan Amount: $350,000\\\\n    * Term: 30 years\\\\n    * Fixed Interest Rate: 4.5%\\\\n    * Property Value: $450,000\\\\n    * Origination Fee: 1%\\\\n    * Other Closing Costs: $3,500\\\\n    \\\\n2. Generate a loan disclosure document and observe the APR calculation\\\\n3. Compare with the manual calculation using the regulatory formula:  \\\\n  APR = 2 \xc3\x97 n \xc3\x97 \\\\\\\\[r \xc3\x97 (1 + r)^n \xc3\xb7 ((1 + r)^n - 1) - i\\\\\\\\]\\\\n\\\\nWhere:\\\\n\\\\n* n is the number of months\\\\n* r is the interest rate per month (annual rate/12)\\\\n* i is the interest-only payment as a percentage of the loan amount\\\\n\\\\n## Expected vs. Actual Results\\\\n\\\\n* Expected APR: 4.72%\\\\n* Actual APR displayed: 4.42% or 4.78% (inconsistent)\\\\n\\\\n## Technical Analysis\\\\n\\\\n* The issue appears to be in the `MortgageLoanCalculationService` class\\\\n* The APR calculation does not correctly factor in all closing costs\\\\n* When using the same input parameters multiple times, different APR results are generated\\\\n* The issue exists in both web and mobile interfaces\\\\n* The discrepancy is more pronounced for jumbo loans (>$500,000)\\\\n\\\\n## Environment\\\\n\\\\n* Production and Testing environments\\\\n* Core Banking Version 3.2.1\\\\n* Issue present since version 3.1.0 deployment on December 10, 2024\\\\n* All mortgage products affected\\\\n\\\\n## Regulatory Implications\\\\n\\\\nThis issue puts us at risk of non-compliance with:\\\\n\\\\n* Truth in Lending Act (TILA)\\\\n* Regulation Z\\\\n* State-specific mortgage disclosure requirements\\\\n\\\\n## Business Impact\\\\n\\\\n* Loan officers manually recalculating APRs, increasing processing time by \\\\\\\\~20 minutes per application\\\\n* Customer service handling \\\\\\\\~15 calls per day related to this issue\\\\n* Legal department concerned about compliance exposure\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10041\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10041\\",\\"key\\":\\"ECS-17\\",\\"fields\\":{\\"summary\\":\\"Integrate Blockchain Technology for Cross-Border Payment Processing\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-09-30\\",\\"created\\":\\"2025-06-22T09:10:17.769+0200\\",\\"description\\":\\"## Overview\\\\n\\\\nImplement blockchain technology integration within our AI-powered Core Banking system to facilitate faster, more transparent, and cost-effective cross-border payments. This feature will leverage distributed ledger technology to reduce settlement times, lower transaction costs, and enhance transparency in international money transfers.\\\\n\\\\n## Business Need\\\\n\\\\nOur current cross-border payment system relies on traditional correspondent banking networks, resulting in:\\\\n\\\\n* High transaction costs (average $25-45 per transfer)\\\\n* Long settlement times (2-5 business days)\\\\n* Limited visibility into transaction status\\\\n* Frequent reconciliation issues with intermediary banks\\\\n\\\\nBlockchain technology can address these challenges by providing:\\\\n\\\\n* Near real-time settlement (minutes instead of days)\\\\n* Lower transaction costs (estimated 40-60% reduction)\\\\n* End-to-end payment tracking\\\\n* Reduced need for intermediary banks\\\\n* Enhanced compliance through immutable transaction records\\\\n\\\\n## Functional Requirements\\\\n\\\\n1. **Payment Initiation**\\\\n\\\\n    * Support for multiple fiat currencies (USD, EUR, GBP, JPY, etc.)\\\\n    * Integration with existing payment screens and workflows\\\\n    * Automatic selection of optimal payment rail based on cost, speed, and destination\\\\n    \\\\n2. **Settlement Network Integration**\\\\n\\\\n    * Integration with enterprise blockchain networks (e.g., R3 Corda, Ripple, JP Morgan\'s Liink)\\\\n    * Smart contract support for automated compliance checks and payment routing\\\\n    * Liquidity management across multiple corridors\\\\n    \\\\n3. **Compliance & Monitoring**\\\\n\\\\n    * Real-time sanction screening\\\\n    * AML monitoring with AI-powered risk scoring\\\\n    * Regulatory reporting capabilities (SWIFT, SEPA, local requirements)\\\\n    * Audit trails for all transactions\\\\n    \\\\n4. **Reporting & Analytics**\\\\n\\\\n    * Real-time payment status tracking\\\\n    * Cost savings analytics\\\\n    * Settlement time metrics\\\\n    * Liquidity utilization reports\\\\n    * Exception handling statistics\\\\n    \\\\n\\\\n## Technical Requirements\\\\n\\\\n1. **Integration Points**\\\\n\\\\n    * Core banking system (payment initiation, account updates)\\\\n    * Blockchain networks (transaction submission, confirmation)\\\\n    * Currency conversion services\\\\n    * Compliance systems\\\\n    * Banking partner APIs\\\\n    \\\\n2. **Security Considerations**\\\\n\\\\n    * Private key management\\\\n    * Node security\\\\n    * Network access controls\\\\n    * Encryption of sensitive data\\\\n    \\\\n3. **Performance Requirements**\\\\n\\\\n    * Support for 1000+ transactions per minute\\\\n    * Transaction confirmation within 30 seconds\\\\n    * 99.99% uptime\\\\n    \\\\n\\\\n## Success Criteria\\\\n\\\\n1. Reduce average cross-border payment settlement time from 3 days to under 1 hour\\\\n2. Decrease transaction costs by at least 40%\\\\n3. Improve payment tracking capabilities with 100% visibility into transaction status\\\\n4. Achieve regulatory compliance across all supported jurisdictions\\\\n5. Process at least 50% of eligible cross-border payments through blockchain rails within 6 months of launch\\\\n\\\\n## Timeline & Milestones\\\\n\\\\nPhase 1 (Research & Planning): 2 months  \\\\nPhase 2 (Development): 4 months  \\\\nPhase 3 (Testing & Compliance): 3 months  \\\\nPhase 4 (Pilot Launch): 2 months  \\\\nPhase 5 (Full Production): 1 month\\\\n\\\\nTarget completion date: September 30, 2024\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/3\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/medium_new.svg\\",\\"name\\":\\"Medium\\",\\"id\\":\\"3\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10040\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10040\\",\\"key\\":\\"ECS-16\\",\\"fields\\":{\\"summary\\":\\"Bug: Incorrect Foreign Currency Exchange Rates in International Transfers\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:08:10.562+0200\\",\\"duedate\\":\\"2024-07-15\\",\\"description\\":\\"## Bug Description\\\\n\\\\nOur AI-powered Core Banking system is displaying and using incorrect foreign currency exchange rates for international transfers. The rates being applied are consistently 0.5-1.2% lower than the actual market rates we should be offering based on our provider agreements.\\\\n\\\\n## Impact\\\\n\\\\n* Financial loss to the bank on transactions where we\'re offering better-than-intended rates\\\\n* Potential compliance issues with regulatory reporting of FX transactions\\\\n* Inconsistency between rates displayed in different parts of the application\\\\n* Customer confusion when seeing different rates pre- and post-transaction\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Log in to the banking portal or mobile app\\\\n2. Navigate to \\\\\\"International Transfers\\\\\\" section\\\\n3. Select any currency pair (most noticeable in EUR/GBP and USD/JPY pairs)\\\\n4. Compare displayed exchange rate with the rate in the administrative backend and the rate from our FX provider API\\\\n\\\\n## Environment\\\\n\\\\n* Production and UAT environments affected\\\\n* Mobile app versions 4.2.1 and web portal\\\\n* Issue first noticed on July 3, 2024\\\\n* All regions and all currency pairs affected\\\\n\\\\n## Technical Details\\\\n\\\\n* The exchange rate API calls are returning correct values (confirmed in logs)\\\\n* The API response data is being cached correctly in Redis\\\\n* The issue appears to be in the rate calculation logic in the `ExchangeRateService.calculateClientRate()` method\\\\n* Legacy code is still using the deprecated `ForexCalculationUtil` which has known precision issues\\\\n* Log files show successful API calls but incorrect rate calculations\\\\n\\\\n## Business Impact\\\\n\\\\n* Estimated financial impact: $15,000-20,000 daily\\\\n* Customer complaints have increased by 15% in the past week\\\\n* Operations team is having to manually adjust rates for high-value transfers\\\\n\\\\n## Priority\\\\n\\\\nHigh - This is causing direct financial loss to the business and affecting customer experience\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10039\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10039\\",\\"key\\":\\"ECS-15\\",\\"fields\\":{\\"summary\\":\\"Develop Next-Generation AI-Powered Fraud Detection System\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T09:06:29.520+0200\\",\\"duedate\\":\\"2024-04-30\\",\\"description\\":\\"## Overview\\\\n\\\\nWe need to develop and implement a comprehensive next-generation AI-powered fraud detection system for our core banking platform. This system should leverage machine learning, behavioral analytics, and real-time transaction monitoring to identify and prevent fraudulent activities across all banking channels.\\\\n\\\\n## Business Need\\\\n\\\\nThe rise of sophisticated banking fraud techniques necessitates an advanced fraud detection system. Our current rule-based system has a high false positive rate (approximately 23%) and fails to detect emerging fraud patterns quickly enough.\\\\n\\\\n## Requirements\\\\n\\\\n### Technical Requirements\\\\n\\\\n* Implement machine learning models for anomaly detection\\\\n* Real-time transaction scoring using multiple data points (device info, geolocation, behavioral patterns, etc.)\\\\n* Seamless integration with the existing transaction processing pipeline\\\\n* Ability to handle 10,000+ transactions per second\\\\n* Low latency (< 100ms) for fraud checks to avoid impacting user experience\\\\n* Integration with third-party data sources for enhanced verification\\\\n* Self-learning capabilities to adapt to new fraud patterns\\\\n\\\\n### Functional Requirements\\\\n\\\\n* Multi-channel fraud detection (mobile, web, ATM, POS)\\\\n* Configurable risk thresholds by product/service type\\\\n* Batch and real-time processing options\\\\n* Customer risk profiling and segmentation\\\\n* Case management system for fraud analysts\\\\n* Automated action mechanisms based on risk scores\\\\n* Visual analytics dashboard for fraud monitoring and pattern recognition\\\\n\\\\n### Security Requirements\\\\n\\\\n* End-to-end encryption of all sensitive data\\\\n* Compliance with GDPR, PCI-DSS, and other relevant regulations\\\\n* Audit trails for all system actions\\\\n* Role-based access control\\\\n\\\\n## Success Metrics\\\\n\\\\n* Reduce false positives by at least 50%\\\\n* Increase fraud detection rate by at least 30%\\\\n* Decrease manual review time by 40%\\\\n* ROI through fraud prevention should exceed system costs by 5x\\\\n\\\\n## Integration Points\\\\n\\\\n* Core banking system\\\\n* Customer data platform\\\\n* Payment processing systems\\\\n* Mobile and internet banking channels\\\\n* Card management system\\\\n* Third-party data providers\\\\n\\\\n## Timeline\\\\n\\\\nThis is a high-priority initiative with a target completion date of April 30, 2024.\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"'
  response chunk 15: b'name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10038\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10038\\",\\"key\\":\\"ECS-14\\",\\"fields\\":{\\"summary\\":\\"Critical Bug: Delay in Credit Card Transaction Processing During Peak Hours\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-02-01\\",\\"created\\":\\"2025-06-22T09:05:17.480+0200\\",\\"description\\":\\"## Bug Description\\\\n\\\\nDuring peak hours (10:00 AM - 2:00 PM local time), credit card transactions are experiencing significant delays in processing. Transactions are taking between 45-120 seconds to complete, when normal processing time should be under 3 seconds.\\\\n\\\\n## Impact\\\\n\\\\n* Customer complaints have increased by 27% in the last week\\\\n* Potential revenue loss as customers abandon transactions\\\\n* Increased load on customer service team dealing with inquiries\\\\n* Reputation damage for reliability of our banking platform\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Process any credit card transaction between 10:00 AM - 2:00 PM local time\\\\n2. Observe the processing time in the transaction logs\\\\n3. Compare with processing times during off-peak hours\\\\n\\\\n## Observed Behavior\\\\n\\\\n* Transaction processing time: 45-120 seconds\\\\n* System logs show database connection pool exhaustion\\\\n* High CPU usage on transaction processing servers\\\\n* Timeout errors in approximately 12% of cases\\\\n\\\\n## Expected Behavior\\\\n\\\\n* Transaction processing time under 3 seconds\\\\n* Stable response times regardless of system load\\\\n* No timeout errors\\\\n\\\\n## Environment Details\\\\n\\\\n* Production environment\\\\n* AI-powered Core Banking v2.3.7\\\\n* Oracle Database 19c\\\\n* Application servers: 8 instances of r5.xlarge on AWS\\\\n\\\\n## Diagnostic Information\\\\n\\\\n* Error logs attached as error_logs_20230823.zip\\\\n* Database performance metrics show high wait times on critical indexes\\\\n* Network latency appears normal\\\\n\\\\n## Priority\\\\n\\\\nCritical - This is affecting a large number of customers and has financial implications\\\\n\\\\n## Suggested Fixes\\\\n\\\\n* Investigate connection pool settings\\\\n* Review database index optimization\\\\n* Consider adding additional processing capacity during peak hours\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10037\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10037\\",\\"key\\":\\"ECS-13\\",\\"fields\\":{\\"summary\\":\\"Implement Biometric Authentication for Mobile Banking App\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2023-11-15\\",\\"created\\":\\"2025-06-22T09:03:57.466+0200\\",\\"description\\":\\"## Overview\\\\n\\\\nWe need to enhance the security of our AI-powered Core Banking system by implementing advanced biometric authentication for the mobile banking app.\\\\n\\\\n## Requirements\\\\n\\\\n* Support fingerprint, facial recognition, and voice pattern authentication\\\\n* Seamless integration with existing login flow\\\\n* Fallback mechanisms for when biometric authentication fails\\\\n* Secure storage of biometric tokens\\\\n* Compliance with GDPR, PSD2, and other relevant banking regulations\\\\n* User option to enable/disable biometric authentication\\\\n\\\\n## Technical Considerations\\\\n\\\\n* Integration with iOS and Android native biometric APIs\\\\n* Security review for potential vulnerabilities\\\\n* Performance testing on various device types\\\\n* Encryption of biometric data in transit and at rest\\\\n\\\\n## Success Criteria\\\\n\\\\n* 99.9% authentication success rate\\\\n* Authentication time under 2 seconds\\\\n* Positive user feedback in beta testing\\\\n* Passing all security audits\\\\n\\\\n## Due Date\\\\n\\\\nNovember 15, 2023\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10036\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10036\\",\\"key\\":\\"ECS-12\\",\\"fields\\":{\\"summary\\":\\"Bug: Transaction Timeout in High-Volume Payment Processing Scenarios\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"created\\":\\"2025-06-22T08:26:35.017+0200\\",\\"duedate\\":\\"2024-07-31\\",\\"description\\":\\"## Bug Description\\\\n\\\\nThe AI-powered Core Banking system experiences transaction timeouts and occasional failed transactions during high-volume processing periods. This is particularly evident during peak hours (10-11 AM and 3-4 PM) and month-end processing cycles.\\\\n\\\\n## Impact\\\\n\\\\n* Approximately 3-5% of transactions fail during peak hours\\\\n* Customer complaints about delayed or missing payments\\\\n* Inconsistent transaction statuses requiring manual reconciliation\\\\n* Increased load on customer service teams\\\\n* Potential regulatory reporting issues for failed settlements\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Run the transaction load simulator with 1000+ concurrent transactions\\\\n2. Observe system performance degradation after \\\\\\\\~800 concurrent transactions\\\\n3. Monitor database connection pool utilization exceeding 95%\\\\n4. Observe transaction timeouts in the application logs with error code ERR-5302\\\\n5. Note that failed transactions show \\\\\\"Processing\\\\\\" status but never complete\\\\n\\\\n## Expected Behavior\\\\n\\\\nThe system should handle at least 2000 concurrent transactions as per the technical specifications without timeouts or performance degradation.\\\\n\\\\n## Actual Behavior\\\\n\\\\n* System starts experiencing timeouts after \\\\\\\\~800 concurrent transactions\\\\n* Database connection pool exhausts\\\\n* Deadlocks occur in the transaction processing pipeline\\\\n* Some transactions remain in \\\\\\"Processing\\\\\\" state indefinitely\\\\n* Error logs show increasing number of \\\\\\"Transaction timeout after 30 seconds\\\\\\" messages\\\\n\\\\n## Technical Details\\\\n\\\\n* Error appears in logs as: `ERR-5302: Transaction timeout after 30s for txn_id=<ID>`\\\\n* Database server CPU utilization spikes to 95%+ during failures\\\\n* Connection pool metrics show 100% utilization\\\\n* Thread dumps show locks in `TransactionProcessingService.java`\\\\n* Database deadlocks observed in transaction tables\\\\n\\\\n## Environment\\\\n\\\\n* Production environment\\\\n* Application Server: Kubernetes cluster with 8 pods, 4 CPU/16GB each\\\\n* Database: Aurora PostgreSQL r5.4xlarge\\\\n* Current Load: \\\\\\\\~500 transactions per second at peak\\\\n* Backend Version: 2.8.3\\\\n* Middleware Version: 1.4.2\\\\n\\\\n## Severity\\\\n\\\\nCritical - Financial impact and service degradation\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10035\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10035\\",\\"key\\":\\"ECS-11\\",\\"fields\\":{\\"summary\\":\\"Customer-facing Personalized Financial Insights Dashboard with Predictive Analytics\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-05-15\\",\\"created\\":\\"2025-06-22T08:25:05.176+0200\\",\\"description\\":\\"## Feature Overview\\\\n\\\\nDevelop a personalized financial insights dashboard for retail customers that leverages AI to analyze spending patterns, predict future financial trends, and provide actionable financial advice.\\\\n\\\\n## Business Case\\\\n\\\\n* Increase mobile app engagement by 30% through personalized insights\\\\n* Reduce customer churn by providing value-added services\\\\n* Create upsell opportunities for financial products based on predictive insights\\\\n* Differentiate our banking platform with next-gen financial management tools\\\\n\\\\n## Detailed Requirements\\\\n\\\\n### User Experience\\\\n\\\\n* Intuitive dashboard showing financial health score (0-100)\\\\n* Personalized spending analytics with category breakdown\\\\n* Interactive cash flow projections '
  response chunk 16: b'(30/60/90 days)\\\\n* \\\\\\"What-if\\\\\\" scenario modeling for major financial decisions\\\\n* Custom financial goals with AI-generated achievement plans\\\\n* Smart notifications for unusual spending or saving opportunities\\\\n\\\\n### AI/ML Components\\\\n\\\\n* Spending pattern recognition algorithm\\\\n* Income stability and growth prediction models\\\\n* Custom financial health scoring system\\\\n* Anomaly detection for fraud prevention\\\\n* Natural language processing for financial advice generation\\\\n* Peer comparison benchmarking (anonymized)\\\\n\\\\n### Technical Requirements\\\\n\\\\n* Real-time data processing pipeline\\\\n* Secure access to transaction history\\\\n* Mobile-responsive design with offline capabilities\\\\n* Data visualization library integration (D3.js or similar)\\\\n* Configurable privacy settings for different insight levels\\\\n* API integration with third-party financial tools (optional)\\\\n\\\\n## Success Metrics\\\\n\\\\n* 50% of active users engaging with insights weekly\\\\n* 25% improvement in customer financial health scores over 6 months\\\\n* 15% increase in adoption of recommended financial products\\\\n* 90% positive feedback on insights relevance and accuracy\\\\n\\\\n## Implementation Phases\\\\n\\\\n1. MVP with basic spending insights and financial health score\\\\n2. Advanced predictive features and goal setting\\\\n3. Social features and third-party integrations\\\\n\\\\n## Dependencies\\\\n\\\\n* Access to transaction categorization engine\\\\n* Machine learning infrastructure\\\\n* Customer data consent framework\\\\n* Mobile app integration capability\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/3\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/medium_new.svg\\",\\"name\\":\\"Medium\\",\\"id\\":\\"3\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10034\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10034\\",\\"key\\":\\"ECS-10\\",\\"fields\\":{\\"summary\\":\\"Bug: Loan Interest Calculation Error for Adjustable Rate Mortgages\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10007\\",\\"id\\":\\"10007\\",\\"description\\":\\"Bugs track problems or errors.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10303?size=medium\\",\\"name\\":\\"Bug\\",\\"subtask\\":false,\\"avatarId\\":10303,\\"entityId\\":\\"0a331a6b-28d8-4142-95d3-0f4b59232598\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2024-02-28\\",\\"created\\":\\"2025-06-22T08:23:29.334+0200\\",\\"description\\":\\"## Bug Description\\\\n\\\\nCritical calculation error identified in the loan servicing module: incorrect interest calculation for Adjustable Rate Mortgages (ARMs) when the rate changes during the repayment period.\\\\n\\\\n## Impact\\\\n\\\\n* Incorrect loan balance calculations for approximately 2,500 mortgage accounts\\\\n* Average discrepancy of 0.35% in interest applied\\\\n* Reporting inconsistencies in monthly financial statements\\\\n* Customer complaints regarding unexpected payment amount changes\\\\n\\\\n## Steps to Reproduce\\\\n\\\\n1. Create a test ARM loan account with initial rate of 4.5%\\\\n2. Process 12 monthly payments\\\\n3. Apply rate change to 5.25% as per ARM terms\\\\n4. Process subsequent monthly payment\\\\n5. Compare calculated interest with expected amount\\\\n6. Observe that the system applies the new rate to the entire loan history rather than from the change date forward\\\\n\\\\n## Expected Behavior\\\\n\\\\nThe system should apply the initial interest rate for payments before the change date and the new rate only for payments after the rate change '
  response chunk 17: b'date.\\\\n\\\\n## Actual Behavior\\\\n\\\\nThe system recalculates all historical interest using the new rate, resulting in incorrect loan balance and payment allocations.\\\\n\\\\n## Technical Details\\\\n\\\\n* Issue occurs in the `LoanInterestCalculator.java` class, likely around line 342\\\\n* Debug logs show incorrect date ranges being passed to the calculation function\\\\n* Error appears in production version 2.8.3\\\\n* Database query reveals 2,543 affected accounts\\\\n\\\\n## Severity\\\\n\\\\nHigh - Financial impact and regulatory compliance concerns\\\\n\\\\n## Environment\\\\n\\\\n* Production environment\\\\n* Server: AWS EC2 r5.xlarge\\\\n* Database: Aurora PostgreSQL 13.4\\\\n* Backend Version: 2.8.3\\\\n* Frontend Version: 3.2.1\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/2\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/high_new.svg\\",\\"name\\":\\"High\\",\\"id\\":\\"2\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}},{\\"expand\\":\\"renderedFields,names,schema,operations,editmeta,changelog,versionedRepresentations\\",\\"id\\":\\"10033\\",\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issue/10033\\",\\"key\\":\\"ECS-9\\",\\"fields\\":{\\"summary\\":\\"AI-powered Fraud Detection Enhancement with Real-time Transaction Risk Scoring\\",\\"issuetype\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/issuetype/10005\\",\\"id\\":\\"10005\\",\\"description\\":\\"A broad piece of functionality.\\",\\"iconUrl\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/2/universal_avatar/view/type/issuetype/avatar/10314?size=medium\\",\\"name\\":\\"Feature\\",\\"subtask\\":false,\\"avatarId\\":10314,\\"entityId\\":\\"1d535a76-3c8f-4532-837b-921fc22d56a2\\",\\"hierarchyLevel\\":0},\\"duedate\\":\\"2023-12-15\\",\\"created\\":\\"2025-06-22T08:22:23.599+0200\\",\\"description\\":\\"## Overview\\\\n\\\\nEnhance our AI-powered Core Banking system\'s fraud detection capabilities by implementing real-time transaction risk scoring using advanced machine learning algorithms.\\\\n\\\\n## Business Value\\\\n\\\\n* Reduce fraud losses by an estimated 35% through early detection\\\\n* Decrease false positives by 40% compared to rule-based systems\\\\n* Improve customer experience by minimizing legitimate transaction declines\\\\n\\\\n## Requirements\\\\n\\\\n1. Implement ML models to analyze transaction patterns in real-time\\\\n2. Create a risk scoring system (0-100) for each transaction with configurable thresholds\\\\n3. Develop an adaptive learning system that improves over time based on confirmed fraud cases\\\\n4. Integrate with existing transaction processing without increasing latency beyond 50ms\\\\n5. Design a dashboard for fraud analysts to review flagged transactions\\\\n6. Implement API endpoints for third-party fraud detection systems integration\\\\n\\\\n## Technical Specifications\\\\n\\\\n* Use TensorFlow or PyTorch for model development\\\\n* Real-time scoring must complete within 100ms\\\\n* Maintain model version control and facilitate A/B testing capabilities\\\\n* Support batch retraining of models using historical data\\\\n* Implement feature store for efficient ML operation\\\\n\\\\n## Success Criteria\\\\n\\\\n* False positive rate under 5%\\\\n* Detection rate above 85% for known fraud patterns\\\\n* System uptime of 99.99%\\\\n* Customer satisfaction metrics maintained or improved\\\\n\\\\n## Dependencies\\\\n\\\\n* Access to historical transaction data\\\\n* Integration with notification systems\\\\n* Compliance review for data usage\\\\n\\\\n\\",\\"priority\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/priority/1\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/priorities/highest_new.svg\\",\\"name\\":\\"Highest\\",\\"id\\":\\"1\\"},\\"status\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/status/10000\\",\\"description\\":\\"\\",\\"iconUrl\\":\\"https://mcp-tests.atlassian.net/images/icons/statuses/generic.png\\",\\"name\\":\\"To Do\\",\\"id\\":\\"10000\\",\\"statusCategory\\":{\\"self\\":\\"https://api.atlassian.com/ex/jira/ba9a141c-9295-4fd5-ac2f-35ca28f0897c/rest/api/3/statuscategory/2\\",\\"id\\":2,\\"key\\":\\"new\\",\\"colorName\\":\\"blue-gray\\",\\"name\\":\\"To Do\\"}}}}],\\"isLast\\":true}"}]}}\n\n'
 Based on the search results, I'll now create a list of all Jira tickets that are not closed but past their due date. Here's the formatted list with ticket ID, title, and a summary for each:

 # Past Due Jira Tickets

 ## 1. ECS-9: AI-powered Fraud Detection Enhancement with Real-time Transaction Risk Scoring
 **Due Date:** December 15, 2023  
 **Status:** To Do  
 **Summary:** This high-priority feature aims to enhance the core banking system with advanced fraud detection capabilities using machine learning. It requires implementing real-time transaction risk scoring with a 0-100 scale, adaptive learning capabilities, and integration with existing systems. The project promises to reduce fraud losses by 35% while decreasing false positives by 40%. Technical requirements include using TensorFlow/PyTorch, completing scoring within 100ms, and maintaining model version control. Success metrics include keeping false positives under 5% with a detection rate above 85% for known fraud patterns.

 ## 2. ECS-13: Implement Biometric Authentication for Mobile Banking App
 **Due Date:** November 15, 2023  
 **Status:** To Do  
 **Summary:** This feature enhances security by implementing advanced biometric authentication for the mobile banking app. Requirements include support for fingerprint, facial recognition, and voice pattern authentication with seamless integration into the existing login flow. The implementation must include fallback mechanisms, secure storage of biometric tokens, and compliance with regulations like GDPR and PSD2. Success criteria include achieving 99.9% authentication success rate, authentication time under 2 seconds, positive user feedback, and passing all security audits. The implementation will require integrating with native iOS and Android biometric APIs.

 ## 3. ECS-21: AI-Powered Fraud Detection for Real-time Transaction Monitoring
 **Due Date:** October 15, 2023  
 **Status:** To Do  
 **Summary:** This highest-priority feature aims to implement AI-driven fraud detection for real-time transaction monitoring. The module will analyze transactions using machine learning, create dynamic customer behavior profiles, identify anomalies based on various factors, and generate real-time alerts with risk scoring. Technical requirements include processing transactions with latency under 100ms, supporting both rule-based and ML-based detection algorithms, and providing a dashboard for visualization. Success metrics include reducing false positives by 40%, increasing fraud detection rate by 25%, and maintaining 99.99% system availability.

 ## 4. ECS-22: System Timeout During Batch Loan Processing for Large Corporate Clients
 **Due Date:** December 10, 2023  
 **Status:** To Do  
 **Summary:** This high-priority bug causes the system to timeout during overnight batch processing of corporate loans when handling portfolios with more than 500 active loans. The issue results in incomplete interest calculations and affects next-day reporting. Symptoms include timeout errors after 12 minutes of processing with error code ERR-3092, resulting in incomplete batch jobs and incorrect outstanding balances in reports. The current workaround involves manually splitting large portfolios into smaller batches, requiring overnight staff and delaying batch cycle completion. The issue has critical impact on financial reporting for large corporate clients.

 ## 5. ECS-28: AI-Powered Real-Time Fraud Detection System for International Wire Transfers
 **Due Date:** December 15, 2023  
 **Status:** To Do  
 **Summary:** This high-priority feature aims to implement an AI-powered fraud detection system specifically for international wire transfers. The system needs to analyze transfer patterns, detect anomalies, and prevent fraudulent transactions before completion. Requirements include implementing machine learning models to analyze transaction patterns, creating a real-time decision engine capable of making decisions within 500ms, and developing a comprehensive risk scoring mechanism. The system must integrate with existing payment networks and include a dashboard for compliance teams to review flagged transactions.

 ## 6. ECS-14: Critical Bug: Delay in Credit Card Transaction Processing During Peak Hours
 **Due Date:** February 1, 2024  
 **Status:** To Do  
 **Summary:** This highest-priority bug causes significant delays in credit card transaction processing during peak hours (10:00 AM - 2:00 PM). Transactions are taking 45-120 seconds to complete instead of the normal under 3 seconds processing time. Impact includes increased customer complaints (27% increase), potential revenue loss from abandoned transactions, and reputation damage. System logs show database connection pool exhaustion, high CPU usage, and timeout errors in approximately 12% of cases. The bug affects production environments and the AI-powered Core Banking v2.3.7 system, with suggested fixes including investigating connection pool settings and adding processing capacity.

 ## 7. ECS-23: Implement Personalized Financial Insights Dashboard for Retail Customers
 **Due Date:** February 15, 2024  
 **Status:** To Do  
 **Summary:** This user story focuses on providing retail banking customers with a personalized financial insights dashboard that analyzes spending patterns, savings behavior, and financial goals. Requirements include automated transaction categorization, recurring payment identification, spending analysis by category, and detection of unusual patterns. The interface needs to provide clear visualizations of spending trends, goal progress trackers, and customizable date ranges. The system should also provide recommendations for potential savings and product offerings based on customer behavior. Technical considerations include data privacy compliance and API integration with transaction processing systems.

 ## 8. ECS-10: Bug: Loan Interest Calculation Error for Adjustable Rate Mortgages
 **Due Date:** February 28, 2024  
 **Status:** To Do  
 **Summary:** This high-priority bug involves a critical calculation error in the loan servicing module affecting Adjustable Rate Mortgages when rates change during the repayment period. The system incorrectly applies the new interest rate to the entire loan history rather than only to payments after the change date. This affects approximately 2,500 mortgage accounts with an average discrepancy of 0.35% in interest applied. The issue causes reporting inconsistencies and customer complaints about unexpected payment amount changes. The bug appears to be in the LoanInterestCalculator.java class and has significant financial impact and regulatory compliance concerns.

 ## 9. ECS-24: Intermittent Authentication Failure Causing Payment Processing Delays
 **Due Date:** April 20, 2024  
 **Status:** To Do  
 **Summary:** This highest-priority bug involves intermittent authentication failures during high-volume payment processing periods. The authentication service sporadically rejects valid credentials from the payment gateway during peak times. The issue affects 12-15% of payment transactions, causing delays of 30+ minutes and increased customer support calls. Diagnostic information shows error frequency increases when transaction volume exceeds 5,000 TPS, with the issue beginning after network infrastructure maintenance. The current workaround involves manually increasing authentication service replica count during peak times and forcing token refresh every 10 minutes instead of the standard 60 minutes.

 ## 10. ECS-39: Develop Next-Generation AI-Powered Fraud Detection System
 **Due Date:** April 30, 2024  
 **Status:** To Do  
 **Summary:** This high-priority feature involves developing a comprehensive next-generation AI-powered fraud detection system for the core banking platform. The system needs to leverage machine learning, behavioral analytics, and real-time monitoring to identify fraudulent activities across all banking channels. Technical requirements include implementing machine learning for anomaly detection, real-time transaction scoring, and seamless integration with existing systems. The system must handle 10,000+ transactions per second with low latency (<100ms). Success metrics include reducing false positives by 50%, increasing fraud detection by 30%, and decreasing manual review time by 40%.

 ## 11. ECS-25: Multi-Currency Wallet with AI-Powered FX Optimization
 **Due Date:** June 30, 2024  
 **Status:** To Do  
 **Summary:** This low-priority feature implements a multi-currency wallet in the AI-powered Core Banking system, allowing customers to hold and manage multiple currencies with AI-powered exchange rate optimization. The feature includes supporting at least 15 major currencies, real-time balance display, currency exchange capabilities, and AI-powered optimization features like smart conversion timing recommendations. The implementation requires real-time integration with FX market data providers, compliance with regulations, and enhanced security measures. Success metrics include a 15% adoption rate in the first year, increased foreign exchange revenue by €2M annually, and improvement in customer satisfaction ratings.

 ## 12. ECS-16: Bug: Incorrect Foreign Currency Exchange Rates in International Transfers
 **Due Date:** July 15, 2024  
 **Status:** To Do  
 **Summary:** This high-priority bug involves the system displaying and using incorrect foreign currency exchange rates for international transfers. Rates being applied are consistently 0.5-1.2% lower than actual market rates, causing financial loss to the bank, potential compliance issues, and customer confusion. The issue appears to be in the rate calculation logic in the ExchangeRateService.calculateClientRate() method, with legacy code using deprecated functionality with known precision issues. The estimated financial impact is $15,000-20,000 daily, with customer complaints increasing by 15% in the past week.

 ## 13. ECS-36: Transaction Timeout in High-Volume Payment Processing Scenarios
 **Due Date:** July 31, 2024  
 **Status:** To Do  
 **Summary:** This highest-priority bug causes transaction timeouts and occasional failures during high-volume processing periods. The system experiences degradation after ~800 concurrent transactions, with database connection pool exhaustion and deadlocks in the transaction processing pipeline. Approximately 3-5% of transactions fail during peak hours, causing delays or missing payments and inconsistent transaction statuses requiring manual reconciliation. The issue appears in the logs as "ERR-5302: Transaction timeout after 30s" and occurs when database server CPU utilization spikes to 95%+ during failures. The system should handle 2,000 concurrent transactions but fails at around 800.

 ## 14. ECS-26: Inconsistent Mortgage Payment Calculation for Bi-Weekly Payment Schedule
 **Due Date:** August 15, 2024  
 **Status:** To Do  
 **Summary:** This medium-priority bug affects the mortgage calculation module, which incorrectly calculates bi-weekly payment amounts for certain mortgage products. The system is applying an incorrect day-count convention, resulting in payment miscalculations that affect both customer-facing calculators and the internal loan servicing system. The calculated bi-weekly payment is approximately 3.3% higher than the correct amount, with incorrect interest allocation throughout the loan term. Approximately 2,300 mortgage accounts with bi-weekly payment schedules are affected, with customers being charged incorrect amounts and receiving inaccurate amortization schedules.

 ## 15. ECS-41: Integrate Blockchain Technology for Cross-Border Payment Processing
 **Due Date:** September 30, 2024  
 **Status:** To Do  
 **Summary:** This medium-priority feature aims to integrate blockchain technology into the core banking system to facilitate faster, more transparent, and cost-effective cross-border payments. The implementation will leverage distributed ledger technology to reduce settlement times from days to minutes, lower transaction costs by 40-60%, and enhance payment tracking. Requirements include supporting multiple fiat currencies, integration with enterprise blockchain networks, real-time sanction screening, and comprehensive reporting capabilities. Success criteria include reducing settlement time from 3 days to under 1 hour, decreasing transaction costs by at least 40%, and achieving regulatory compliance across all supported jurisdictions.

 ## 16. ECS-27: Implement Predictive Cash Flow Management for SME Customers
 **Due Date:** October 17, 2024  
 **Status:** To Do  
 **Summary:** This high-priority user story focuses on providing small to medium-sized business owners with AI-powered cash flow predictions and management tools. The feature will help businesses anticipate financial needs, optimize working capital, and avoid liquidity issues. Key requirements include an AI prediction engine that analyzes historical transaction patterns, a comprehensive cash flow dashboard with visual representations of financial position, working capital optimization tools, and integration with accounting software. The system should accurately predict cash position within a 15% margin of error for 30-day forecasts and successfully notify users of predicted shortfalls at least 14 days in advance.

 ## 17. ECS-18: Bug: Inconsistent Mortgage Loan Calculations Leading to Incorrect APR Display
 **Due Date:** December 15, 2024  
 **Status:** To Do  
 **Summary:** This highest-priority bug involves the mortgage loan calculation module producing inconsistent Annual Percentage Rate (APR) figures. The APR displayed to customers is incorrectly calculated by up to 0.3% compared to the rate that should be applied based on regulatory guidelines. The issue creates potential regulatory compliance problems with Truth in Lending Act and Regulation Z, along with customer confusion and legal liability risks. The bug appears to be in the MortgageLoanCalculationService class where APR calculation doesn't correctly factor in all closing costs. The issue has been present since version 3.1.0 deployment on December 10, 2024.

 ## 18. ECS-31: AI-Driven Fraud Detection System with Real-time Transaction Monitoring
 **Due Date:** December 15, 2024  
 **Status:** To Do  
 **Summary:** This highest-priority feature requires implementing an AI-driven fraud detection module with advanced capabilities for the core banking system. The system needs to process and analyze transactions in real-time, provide fraud risk scoring within 100ms, support multiple transaction types, and utilize multi-layered machine learning models. Key components include behavioral analysis, anomaly detection, contextual authentication, and an adaptive rule engine. Technical requirements include support for processing 10,000+ transactions per second with latency under 100ms and 99.99% system uptime. Success metrics include reducing fraud losses by 30%, keeping false positive rates below 2%, and reducing investigation time by 40%.

 ## 19. ECS-29: Critical Bug: Loan Amortization Calculation Error Causing Incorrect Customer Statements
 **Due Date:** April 30, 2024  
 **Status:** To Do  
 **Summary:** This highest-priority bug involves a calculation error in the loan amortization module that incorrectly calculates the principal-to-interest ratio on adjustable-rate mortgage loans. The error affects approximately 3,200 ARM customers who are receiving incorrect monthly statements, with principal payments being under-reported by an average of 2.3% and interest payments being over-reported. The issue appears in the calculateAdjustableRateAmortization() method, which uses the previous interest rate when recalculating after a rate adjustment. The mortgage servicing team has implemented a manual recalculation process for affected customers who call in, but this is not scalable to all accounts.

 ## 20. ECS-20: Bug: Customer Data Synchronization Failures Between Mobile App and Core System
 **Due Date:** May 20, 2025  
 **Status:** To Do  
 **Summary:** This high-priority bug involves customer profile updates made through the mobile banking app intermittently failing to synchronize with the core banking system. This results in inconsistent customer information across different banking channels, with symptoms including address updates not reflecting in branch banking systems and KYC document updates not propagating to compliance monitoring systems. The synchronization issue occurs in the data integration layer between the API gateway and core banking system, with error logs showing intermittent timeouts. The issue affects approximately 15-20% of all customer updates, impacting around 2,500 customers per week.
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