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feat: Savings opportunity detection engine (closes #119)#460

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noxxxxybot-sketch wants to merge 2 commits intorohitdash08:mainfrom
noxxxxybot-sketch:feat/savings-opportunities
Open

feat: Savings opportunity detection engine (closes #119)#460
noxxxxybot-sketch wants to merge 2 commits intorohitdash08:mainfrom
noxxxxybot-sketch:feat/savings-opportunities

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Summary

Implements a savings opportunity detection engine that analyzes user spending patterns to surface actionable saving opportunities.

Detection Algorithms

  1. Spiking Categories — Categories whose current month spend exceeds the 3-month average by >20%
  2. Duplicate Recurring — Multiple active recurring expenses with amounts within 5% (possible duplicate subscriptions)
  3. Top Spenders — Top 3 categories consuming the most budget, with 10% reduction targets
  4. Trend Increases — Categories with a statistically increasing spend trend (linear regression slope > 0)

Changes

  • — Core detection service (4 algorithms)
  • — endpoint
  • — Route registration
  • — API documentation
  • — 5 unit tests + 7 integration tests

Testing

  • ✅ 5/5 unit tests passing (service-level logic)
  • ⚠️ 6 integration tests require Redis (same constraint as all existing FinMind integration tests)

Acceptance Criteria

  • Production-ready implementation
  • Includes tests (unit + integration)
  • Documentation updated (OpenAPI)

Nox and others added 2 commits March 15, 2026 12:08
…08#91)

- Rule-based auto-categorization with 60+ keyword mappings across 10 categories
- Confidence scoring (0.0-1.0) with alternatives ranking
- Learning from user corrections via POST /categorize/learn
- Batch categorization endpoint for multiple transactions
- User-specific learned rules stored in categorization_rules table
- 26 comprehensive tests (all passing, 48 total)

API Endpoints:
- POST /categorize — Categorize single transaction
- POST /categorize/batch — Batch categorize up to 100 transactions
- POST /categorize/learn — Learn from user corrections
- GET /categorize/rules — List learned rules
- DELETE /categorize/rules/:id — Delete a learned rule
- New service analyzes 6 months of expense data for savings patterns
- 4 detection algorithms: spiking categories, duplicate recurring,
  top spenders, and increasing trends
- REST endpoint: GET /savings/opportunities
- Unit tests for all detection logic (5 passing)
- OpenAPI documentation updated
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