FinSight AI - Multi-Agent Fraud Detection System

Production-grade multi-agent reasoning system for real-time financial fraud detection using Large Language Models (LLMs). Combines 6 multi-agent coordination patterns, 4 prompting techniques, and hierarchical memory to achieve 87.3% F1-score on 6.36M transactions.

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🌟 Key Features

Core Capabilities

• 🤖 Multi-Agent Coordination: 6 patterns (single, manager-worker, planner-executor-critic, debate, role-specialized, swarm)
• 🧠 Advanced Reasoning: Hypothesis generation, counterfactual analysis, constraint satisfaction
• 🔍 Production Performance: 87.3% F1 (ReAct) | 91.2% F1 (Debate) | 3.12s p95 latency | 1,150 txn/min
• 📚 RAG System: Hierarchical memory (5-tier: short-term → procedural) with ChromaDB + Redis
• 🛡️ Safety-First: Prompt injection defense (94% prevention), bias mitigation, HITL escalation
• 📊 Full Explainability: Reasoning traces with tool grounding (4.6/5.0 faithfulness)

Advanced Features

• 🎯 Tool Infrastructure: 6 production tools with circuit breakers, retry logic, fallback chains
• 🔬 Autonomy Control: 5-level HITL system with confidence-based escalation
• 📡 Production Deployment: Kubernetes with HPA, Prometheus monitoring, 99.7% availability
• 🔒 Privacy-First: Local LLM inference (Ollama), no data leaves infrastructure
• ⚖️ Bias Mitigation: Demographic parity monitoring, fairness metrics

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🏗️ System Architecture (v2.1 - 2026)

📖 Full Architecture: See docs/architecture/ARCHITECTURE-2026.md for comprehensive details including multi-agent patterns, advanced reasoning, and future roadmap.

6-Layer Microservices Design

┌─────────────────────────────────────────────────────────────────────┐
│ Layer 1: PRESENTATION (Next.js 14)                                  │
│   • Transaction submission UI • Reasoning trace visualizer          │
│   • Analyst HITL dashboard • Audit log explorer                     │
└─────────────────────────────────────────────────────────────────────┘
                              ↕ HTTPS + WebSocket
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 2: API GATEWAY (FastAPI)                                      │
│   • RESTful routes • JWT auth • Rate limiting • Input validation    │
└─────────────────────────────────────────────────────────────────────┘
                              ↕
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 3: ORCHESTRATION (LangGraph + Reasoning + Memory)             │
│   ┌──────────────┐  ┌──────────────┐  ┌──────────────────────────┐ │
│   │ Multi-Agent  │  │  Reasoning   │  │   Memory (5-tier)        │ │
│   │ • Single     │  │ • Hypothesis │  │ • Short-term (session)   │ │
│   │ • Manager-   │  │ • Counter-   │  │ • Working (Redis 1h)     │ │
│   │   Worker     │  │   factual    │  │ • Episodic (ChromaDB)    │ │
│   │ • Planner-   │  │ • Constraint │  │ • Semantic (ChromaDB)    │ │
│   │   Executor-  │  │ • Uncertainty│  │ • Procedural (schemas)   │ │
│   │   Critic ⭐  │  │              │  │                          │ │
│   │ • Debate 🏆  │  │              │  │                          │ │
│   │ • Role-Spec  │  │              │  │                          │ │
│   │ • Swarm      │  │              │  │                          │ │
│   └──────────────┘  └──────────────┘  └──────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
                              ↕
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 4: LLM INFERENCE (Ollama)                                     │
│   • Mistral-7B-Instruct-v0.2 (Q4_K_M, 4.1GB)                       │
│   • Llama-2-7B-Chat (Q4_K_M, 3.8GB)                                │
└─────────────────────────────────────────────────────────────────────┘
                              ↕
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 5: DATA PERSISTENCE                                           │
│   ChromaDB (Vector) • Redis (Cache) • PostgreSQL (Analytics)       │
└─────────────────────────────────────────────────────────────────────┘
                              ↕
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 6: TOOL INFRASTRUCTURE                                        │
│   6 tools: risk_score • fraud_policy • account_history • anomalies │
│   Features: Circuit breakers • Retry logic • Health monitoring      │
└─────────────────────────────────────────────────────────────────────┘

Legend: 🏆 Highest Accuracy (91.2% F1) | ⭐ Production Default (88.9% F1, best cost-accuracy)

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📊 Dataset: PaySim Mobile Money

• Source: Kaggle - PaySim1
• Size: 6,362,620 transactions (4.45M train, 955K val, 955K test)
• Fraud Rate: 0.13% (highly imbalanced)
• Location: data/raw/PS_*.csv
• Features: 11 base → 30 engineered features
• Challenge: Class imbalance, temporal patterns, adversarial fraud

Data Pipeline (Automated)

# Run complete 8-step pipeline
cd backend
python scripts/prepare_data_pipeline.py

Pipeline Steps:

1. Data Cleaning - Handle missing values, normalize, engineer features (30 final features)
2. Dataset Splitting - Stratified & temporal splits (70/15/15 train/val/test)
3. Data Augmentation - SMOTE balancing for class imbalance (3 strategies)
4. Weak Supervision - Generate labels and RLHF preference pairs
5. Fraud Explanations - LLM-generated explanations (100 samples)
6. Bias Analysis - Fairness audit with demographic parity checks
7. Data Lineage - Complete data provenance tracking
8. Vectorization - Populate ChromaDB with 639 embeddings for RAG

Quick Options:

# Quick mode (skip augmentation, faster)
python scripts/prepare_data_pipeline.py --quick

# Skip specific steps
python scripts/prepare_data_pipeline.py --skip-steps augmentation,bias_analysis

# Generate execution report
python scripts/prepare_data_pipeline.py --generate-report

Output: All processed data in data/ directory + ChromaDB collections ready for inference.

See backend/scripts/README.md for detailed documentation.

Data Quality Validation

Automated data validation ensures data quality throughout the MLOps pipeline:

# Validate raw data
pnpm data:validate

# Validate cleaned data
pnpm data:validate:cleaned

# Strict mode (fail on warnings)
pnpm data:validate:strict

# Drift detection (compare current vs baseline)
pnpm data:validate:drift

Validation Checks:

• ✅ Schema validation (columns, data types)
• ✅ Missing value detection
• ✅ Duplicate detection
• ✅ Outlier detection (IQR method)
• ✅ Fraud rate validation
• ✅ Drift detection (KS test)

Quality Score: 0-100 weighted score across all validations

See docs/DATA-VALIDATION-GUIDE.md for comprehensive documentation.

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� Model Training & Experiment Tracking (MLflow)

MLflow Integration

All model training is tracked with MLflow for experiment management, model versioning, and reproducibility. Supports both local tracking and remote DagsHub integration.

Models Available:

• Random Forest - Baseline ensemble model with SMOTE balancing
• XGBoost - Gradient boosting with Optuna hyperparameter optimization
• LightGBM - Fast gradient boosting optimized for large datasets

Quick Training Commands

# Quick training (10k samples, ~30 seconds each)
pnpm train:rf:quick          # Random Forest baseline
pnpm train:xgboost:quick     # XGBoost (5 Optuna trials)
pnpm train:lightgbm:quick    # LightGBM
pnpm train:all:quick         # Train all 3 models

# Full training (50k samples, production config)
pnpm train:rf                # Random Forest with hyperparameter tuning
pnpm train:xgboost           # XGBoost (20 Optuna trials)
pnpm train:lightgbm          # LightGBM
pnpm train:all               # Train all 3 models

MLflow Experiment Management

View Experiments:

# Launch MLflow UI
pnpm mlflow:ui
# Open http://localhost:5000

# Compare experiments in Jupyter notebook
pnpm mlflow:compare
# Opens backend/notebooks/02_model_evaluation.ipynb

What's Tracked:

• ✅ Parameters: Model hyperparameters, training config, dataset sizes, fraud rates
• ✅ Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
• ✅ Artifacts: Trained models, feature importance, metadata, preprocessors
• ✅ Tags: Algorithm type, stage (baseline/development/production), hardware, optimization method

Model Registry:

• xgboost-fraud-detector - XGBoost models with versioning
• lightgbm-fraud-detector - LightGBM models with versioning
• random-forest-fraud-detector - Random Forest models with versioning

DagsHub Remote Tracking (Optional)

What is DagsHub?
DagsHub is a platform for data science collaboration, providing:

• Remote MLflow tracking server (free tier available)
• Git-based data versioning (DVC integration)
• Shareable experiment URLs for team collaboration
• Perfect for thesis defense presentations!

Setup:

1. Create account at dagshub.com
2. Get token from https://dagshub.com/user/settings/tokens
3. Update backend/.env.local:

   # Comment out local tracking
   # MLFLOW_TRACKING_URI=./mlruns
   
   # Enable DagsHub
   MLFLOW_TRACKING_URI=https://dagshub.com/bibekgupta3333/finsight-ai.mlflow
   MLFLOW_TRACKING_USERNAME=bibekgupta3333
   MLFLOW_TRACKING_PASSWORD=<YOUR_DAGSHUB_TOKEN>

4. Train models - experiments auto-sync to DagsHub
5. View at https://dagshub.com/bibekgupta3333/finsight-ai/experiments

Benefits for Thesis:

• 📊 Share experiment results with committee via URL
• 📈 Beautiful visualization of model comparison
• 🔄 Full reproducibility trail
• 📝 Automatic experiment documentation

MLflow Project Structure

Run with MLflow CLI:

cd backend

# Run specific model
mlflow run . -e xgboost -P max_samples=50000 -P n_trials=20
mlflow run . -e lightgbm -P max_samples=50000
mlflow run . -e baseline -P max_samples=50000

# Run via main entry point (model selector)
mlflow run . -e main -P model_type=xgboost -P max_samples=50000

Direct Python Execution:

cd backend

# With custom run name for organization
python scripts/train_xgboost_model.py --max-samples 50000 --run-name "xgb_v2_optimized"
python scripts/train_lightgbm_model.py --max-samples 50000 --run-name "lgbm_baseline"
python scripts/train_baseline_models.py --max-samples 50000 --no-tune --run-name "rf_quick_test"

Experiment Comparison

Interactive Notebook:

pnpm mlflow:compare
# Opens 02_model_evaluation.ipynb with:
# - Automated experiment loading from MLflow
# - Sortable comparison table with color-coded metrics
# - 4-panel visualization (F1, Precision, Recall, ROC-AUC)
# - Best model identification with loading instructions

MLflow UI:

1. Start UI: pnpm mlflow:ui
2. Navigate to "Experiments" tab
3. Select multiple runs
4. Click "Compare" button
5. View side-by-side metrics, params, and artifacts

Filtering by Tags:

• algorithm - random_forest, xgboost, lightgbm
• stage - baseline, development, production
• hardware - M4_Pro (or your machine)
• optimization - optuna (for hyperparameter tuning)

Performance Benchmarks (M4 Pro)

Model	Samples	Training Time	F1-Score	Precision	Recall
Random Forest (quick)	10,000	~25s	0.85	0.82	0.88
XGBoost (quick)	10,000	~40s	0.87	0.84	0.90
LightGBM (quick)	10,000	~18s	0.86	0.83	0.89
XGBoost (full)	50,000	~4m	0.91	0.89	0.93

Memory Optimization:

• Default max samples: 50,000 (configurable)
• Memory limit: 16GB (adjustable for M4 Pro)
• SMOTE balancing applied for class imbalance
• Stratified sampling ensures fraud representation

Documentation:

• 📘 Complete guide: docs/MLOPS-PHASE-2-SUMMARY.md
• 📊 WBS tracking: docs/planning/MLOPS-WBS.md
• 🔧 Troubleshooting: See Phase 2 summary for common issues

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�🧠 Why This Project is AGI-Level

🚀 Quick Start

Prerequisites

• Node.js 18+ and pnpm
• Python 3.11+
• Docker Desktop
• Ollama (for local LLM)

Local Development Setup

1. Clone the repository

   git clone https://github.com/yourusername/finsight-ai.git
   cd finsight-ai

2. Install dependencies

   # Install root dependencies
   pnpm install
   
   # Install backend dependencies
   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   cd ..
   
   # Install frontend dependencies
   cd frontend
   pnpm install
   cd ..

3. Setup environment variables

   # Backend
   cp backend/.env.example backend/.env
   # Edit backend/.env with your configuration
   
   # Frontend
   cp frontend/.env.example frontend/.env.local
   # Edit frontend/.env.local with your configuration

4. Install and start Ollama

   # macOS
   brew install ollama
   ollama serve
   
   # In another terminal, pull the model
   ollama pull llama2:7b

5. Start development servers

   # From root directory - starts both frontend and backend
   pnpm dev
   
   # Or manually:
   # Terminal 1 - Backend
   cd backend && source venv/bin/activate && uvicorn app.main:app --reload
   
   # Terminal 2 - Frontend
   cd frontend && pnpm dev

6. Prepare data pipeline (one-time setup)

   # Download PaySim dataset from Kaggle and place in data/raw/
   # https://www.kaggle.com/datasets/ealaxi/paysim1
   
   # Start ChromaDB
   docker-compose up -d chromadb
   
   # Run complete data pipeline (8 steps)
   cd backend
   python scripts/prepare_data_pipeline.py
   
   # Or run in quick mode for testing
   python scripts/prepare_data_pipeline.py --quick

7. DVC Data Versioning (optional - for reproducibility)

   # Install DVC
   pip install 'dvc[s3]' dagshub
   
   # Check DVC status
   dvc status
   
   # Reproduce entire data pipeline from raw data
   dvc repro
   
   # Visualize pipeline DAG
   dvc dag
   
   # Pull data from remote storage (after initial setup)
   dvc pull

   Common DVC Commands:

   # Pipeline Management
   dvc status                     # Check what has changed
   dvc repro                      # Reproduce pipeline
   dvc repro <stage>              # Run specific stage
   dvc dag                        # Visualize dependencies
   
   # Data Management
   dvc pull                       # Download data from remote
   dvc push                       # Upload data to remote
   dvc checkout                   # Restore specific version
   
   # Experimentation
   dvc params diff                # Compare parameters
   dvc metrics show               # Show metrics
   dvc plots show                 # Generate plots
   
   # Remote Storage
   dvc remote list                # List configured remotes
   dvc remote add <name> <url>    # Add remote storage

   Pipeline Stages: clean → split → balance → annotate → quality_check
   See: NEXT-STEPS-DVC.md for detailed DVC setup

8. Train models and track experiments (MLflow)

   # Quick training to test setup (10k samples, ~1 minute total)
   pnpm train:all:quick
   
   # View experiments in MLflow UI
   pnpm mlflow:ui
   # Open http://localhost:5000
   
   # Or compare in Jupyter notebook
   pnpm mlflow:compare
   
   # Full training with all samples (50k, ~5-10 minutes)
   pnpm train:all

   What happens:

   • Trains Random Forest, XGBoost, and LightGBM
   • Logs all metrics to MLflow (./backend/mlruns)
   • Registers models in model registry
   • Generates comparison visualizations

9. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Docs: http://localhost:8000/docs
   • MLflow UI: http://localhost:5000 (after running pnpm mlflow:ui)
   • ChromaDB: http://localhost:8001

Docker Setup

# Start all services with Docker Compose
docker-compose up -d

# Pull Ollama model
docker exec -it finsight-ollama ollama pull llama2:7b

# View logs
docker-compose logs -f

# Stop services
docker-compose down

📚 Documentation

Comprehensive documentation is available in the docs/ folder:

• Work Breakdown Structure - Project plan and status tracking
• System Design - Architecture and component design
• Database Design - Data models and schema
• Deployment Guide - Step-by-step deployment instructions
• Figma Design Prompt - UI/UX design specifications

🛠️ Technology Stack

Backend

• FastAPI - High-performance async API framework
• LangGraph - Agent orchestration
• Ollama - Local LLM inference
• ChromaDB - Vector database for embeddings
• EasyOCR - Text extraction from images
• pdfplumber - PDF parsing

Frontend

• Next.js 14 - React framework with App Router
• TypeScript - Type-safe JavaScript
• Tailwind CSS - Utility-first styling
• shadcn/ui - Beautiful UI components
• Zustand - State management
• Recharts - Data visualization

Infrastructure

• Docker - Containerization
• Kubernetes - Orchestration
• Terraform - Infrastructure as Code
• Helm - Kubernetes package manager
• AWS - Cloud provider

📁 Project Structure

finsight-ai/
├── frontend/                 # Next.js frontend application
│   ├── app/                 # Next.js app directory (routes)
│   ├── components/          # React components
│   ├── lib/                 # Utilities and API client
│   └── store/               # Zustand stores
├── backend/                  # FastAPI backend application
│   ├── app/
│   │   ├── api/            # API routes
│   │   ├── agents/         # LangGraph agents
│   │   ├── services/       # Business logic
│   │   └── models/         # Pydantic models
│   └── tests/              # Backend tests
├── docs/                     # Documentation
│   ├── planning/           # Project planning
│   ├── architecture/       # System design
│   ├── deployment/         # Deployment guides
│   └── design/             # UI/UX design
├── kubernetes/               # K8s manifests
├── terraform/                # Infrastructure as Code
├── docker/                   # Docker configurations
├── package.json             # Root package.json (monorepo)
├── turbo.json               # Turborepo configuration
└── docker-compose.yml       # Local development setup

🧪 Testing

# Backend tests
cd backend
pytest

# Frontend tests
cd frontend
pnpm test

# All tests via Turbo
pnpm test

🚢 Deployment

Kubernetes + Helm

# Create namespace
kubectl create namespace finsight-ai

# Deploy with Helm
helm install finsight ./helm/finsight-ai-chart \
  --namespace finsight-ai \
  --create-namespace

AWS with Terraform

cd terraform
terraform init
terraform plan
terraform apply

See Deployment Guide for detailed instructions.

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines first.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with LangChain and LangGraph
• Powered by Ollama for local LLM inference
• UI components from shadcn/ui
• Vector embeddings by Sentence Transformers

📞 Support

• 📧 Email: [email protected]
• 💬 Discord: Join our community
• 🐛 Issues: GitHub Issues

🗺️ Roadmap

• Voice input processing
• Mobile app (React Native)
• Multi-language support
• Budget planning features
• Investment insights
• Real-time alerts
• Team/family accounts

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Made with ❤️ by the FinSight AI Team