AI Expense Management
NLP Categorization & Anomaly Detection
89% auto-categorization accuracy with AI-driven spending anomaly detection and custom dashboards.
Context
This project demonstrates full-stack AI product engineering — combining NLP auto-categorization with financial anomaly detection, wrapped in a polished React frontend with interactive data visualizations.
The problem
Manual expense categorization is tedious and error-prone. Users need intelligent auto-categorization they can trust, combined with proactive anomaly detection that surfaces unusual spending patterns without generating false alarms.
How I built it
Built NLP auto-categorization engine using OpenAI API with confidence scoring and manual override UX
Implemented AI-driven spending anomaly detection for proactive alerts
Created custom Chart.js dashboards for spending visualization and trend analysis
Built React + Redux frontend with TypeScript for type-safe development
Designed FastAPI backend with PostgreSQL, JWT auth, and Docker Compose deployment
Why these choices
Confidence scoring with manual override
AI categorization isn't always right. Showing confidence scores lets users trust high-confidence results and correct low-confidence ones — building trust through transparency.
Chart.js for data visualization
Lightweight, highly customizable, and well-suited for financial dashboards without the overhead of heavier visualization libraries.