An AI-powered financial analytics system that transforms raw transaction data into behavioral insights, financial health metrics, and risk intelligence.

Built with a full-stack architecture combining React, FastAPI, SQL, and AI-driven analytics logic.

Most expense trackers only store transactions.
They don’t analyze behavior, detect risks, or provide financial intelligence.

This platform acts as a financial behavior engine, not just an expense tracker.

This system ingests financial data from multiple sources and applies intelligent processing to:

• Automatically categorize expenses
• Calculate financial health score
• Detect risk patterns
• Compare budget vs actual spending
• Generate behavioral recommendations

flowchart LR
    A[Frontend - React Dashboard] -->|API Calls| B[FastAPI Backend]
    B --> C[Service Layer]
    C --> D[AI Categorization Engine]
    C --> E[Analytics Engine]
    C --> F[Budget Engine]
    D --> G[(Database)]
    E --> G
    F --> G

• React + TypeScript

• Recharts (Data Visualization)

• Axios (API communication)

• Tailwind CSS

• FastAPI

• SQLAlchemy ORM

• Pydantic Schemas

• REST API Architecture

• AI / Data Intelligence

• Rule-based NLP categorization engine

• Financial risk analysis logic

• Behavioral scoring model

• Aggregation analytics pipeline

• SQL (Mysql / Postgres-ready)

sequenceDiagram
    User->>Frontend: Upload CSV / Add Transaction
    Frontend->>Backend: API Request
    Backend->>Categorization Service: Classify Expense
    Backend->>Database: Store Transaction
    Backend->>Analytics Engine: Compute Metrics
    Analytics Engine->>Backend: Insights
    Backend->>Frontend: Dashboard Data

• Text cleaning & normalization

• Keyword-based intelligent matching

• Confidence scoring

• Fallback classification

• "Swiggy food order" → FOOD (confidence: 0.74)

• Financial Health Score Gauge

• Category-wise Spending Chart

• Budget vs Actual Pie Chart

• Risk Alerts Panel

• cd backend
• pip install -r requirements.txt
• uvicorn app.main:app --reload

• cd frontend
• npm install
• npm run dev

• ✔ Modular service-layer architecture
• ✔ Clean API contracts
• ✔ Scalable analytics pipeline
• ✔ AI logic separated from routes
• ✔ Frontend-backend integration
• ✔ Production-style project structure

• ML-based expense prediction

• Bank SMS auto parsing

• Anomaly detection using ML

• User authentication

• Cloud deployment

Full Stack + AI Systems Developer