https://github.com/designer-coderajay/bfsi-credit-intelligence
Agentic AI loan underwriting platform for Indian BFSI sector. LangGraph v0.3 + 5 MCP servers (Bureau, GST, KYC, RBI Compliance, Account Aggregator) + XGBoost + SHAP + Kafka. RBI/DPDP 2023 compliant. Deployed on AWS Mumbai (ap-south-1).
https://github.com/designer-coderajay/bfsi-credit-intelligence
agentic-ai bfsi fastapi fintech kafka langgraph loan-underwriting mcp mlflow mlops ocr python rbi-compliance shap xgboost
Last synced: 5 days ago
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Agentic AI loan underwriting platform for Indian BFSI sector. LangGraph v0.3 + 5 MCP servers (Bureau, GST, KYC, RBI Compliance, Account Aggregator) + XGBoost + SHAP + Kafka. RBI/DPDP 2023 compliant. Deployed on AWS Mumbai (ap-south-1).
- Host: GitHub
- URL: https://github.com/designer-coderajay/bfsi-credit-intelligence
- Owner: designer-coderajay
- Created: 2026-05-01T12:21:00.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2026-05-01T17:36:00.000Z (about 1 month ago)
- Last Synced: 2026-05-01T19:19:41.328Z (about 1 month ago)
- Topics: agentic-ai, bfsi, fastapi, fintech, kafka, langgraph, loan-underwriting, mcp, mlflow, mlops, ocr, python, rbi-compliance, shap, xgboost
- Language: Python
- Size: 78.1 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# BFSI Credit Intelligence — Agentic Loan Underwriting Platform
> **Production-grade agentic AI platform for real-time loan underwriting in Indian BFSI sector.**
> Built with LangGraph v0.3, MCP (Model Context Protocol), XGBoost + SHAP, and RBI-compliance guardrails.
---
## What This Does
An end-to-end loan underwriting system that replaces a 3-5 day manual process with a **< 5 second AI decision**:
1. Applicant submits loan application with documents
2. OCR pipeline extracts data (English + Hindi via Surya OCR)
3. 6 specialized LangGraph agents run in parallel:
- **Document Validator** → verifies KYC documents, OCR extraction
- **Financial Analyst** → income, obligations, DTI ratio
- **Credit Scorer** → XGBoost model + CIBIL bureau (parallel)
- **Fraud Detector** → Isolation Forest + rule engine (parallel)
- **Compliance Checker** → RBI Master Directions, DPDP Act 2023
- **Decision Agent** → final underwriting decision
4. SHAP values explain every decision (regulator-ready)
5. Decision published to Kafka → audit log → PostgreSQL
---
## Architecture
```
┌─────────────┐ ┌──────────────────────────────────────────────────┐
│ Next.js │ │ LangGraph StateGraph │
│ Dashboard │───▶│ │
│ Port 3000 │ │ [Doc Validator] → [Financial Analyst] │
└─────────────┘ │ ↙ ↘ │
│ [Credit Scorer] [Fraud Detector] ← parallel │
FastAPI │ ↘ ↙ │
Port 8000 │ [Compliance Checker] → [Decision Agent] │
│ │ interrupt_before=["decision_agent"] │
│ └──────────────────────────────────────────────────┘
│
┌──────┴──────┐ ┌─────────────────────────────────────────┐
│ MCP Servers │ │ Infrastructure │
│ Bureau 9001 │ │ PostgreSQL 16 │ Redis 7 │
│ BankTxn9002 │ │ Kafka (MSK) │ MLflow │
│ GST 9003 │ │ Qdrant Vector │ Evidently Monitoring │
│ RBI 9004 │ │ Airflow DAGs │ Prometheus + Grafana │
│ PennyDp9005 │ └─────────────────────────────────────────┘
└─────────────┘
```
---
## Key Technical Stack
| Layer | Technology |
|-------|-----------|
| Agent Orchestration | LangGraph v0.3 (StateGraph, parallel fan-out, human-in-loop) |
| LLM | Claude Sonnet (Anthropic) via LangChain |
| MCP Servers | FastMCP + Streamable HTTP transport (5 servers) |
| Credit Model | XGBoost + Optuna HPO + SMOTE + SHAP |
| Fraud Detection | Isolation Forest + rule engine |
| OCR | PyMuPDF + Surya OCR (Hindi + English) |
| Streaming | Apache Kafka (Confluent) |
| API | FastAPI (async) |
| Frontend | Next.js 14 App Router + Tailwind |
| MLOps | MLflow + Evidently + Airflow (weekly retrain) |
| Infra | EKS (ap-south-1) + Terraform + GitHub Actions |
| Compliance | RBI Master Directions, DPDP Act 2023, PMLA 2002 |
---
## Quick Start
```bash
# Clone and setup
cd ~/Desktop/bfsi-credit-intelligence
cp .env.example .env
# Fill in your ANTHROPIC_API_KEY
# Start everything
make dev-full # Docker Compose: all services
make kafka-topics # Create required Kafka topics
make train-all # Train credit + fraud ML models
# Or run backend only
pip install -r requirements.txt
make mcp-start # Start all 5 MCP servers
make dev # Start FastAPI on :8000
cd frontend && npm install && npm run dev # Next.js on :3000
```
---
## MCP Servers
| Server | Port | Tools | Purpose |
|--------|------|-------|---------|
| `bureau_mcp` | 9001 | `fetch_bureau_score`, `fetch_bureau_report_details` | CIBIL/Experian credit bureau data |
| `bank_txn_mcp` | 9002 | `fetch_bank_statement`, `detect_obligations` | AA Framework — account aggregation |
| `gst_mcp` | 9003 | `verify_gstin`, `fetch_gst_returns` | MSME/business loan GST verification |
| `rbi_compliance_mcp` | 9004 | `check_rbi_compliance`, `get_fair_lending_guidelines` | RBI Master Directions validator |
| `penny_drop_mcp` | 9005 | `verify_pan`, `verify_bank_account`, `verify_aadhaar_otp`, `check_ckyc` | KYC identity verification |
---
## Agentic Flow Detail
```python
# Parallel fan-out after Financial Analyst
graph.add_edge("financial_analyst", "credit_scorer")
graph.add_edge("financial_analyst", "fraud_detector")
# Human-in-the-loop before final decision
graph.compile(interrupt_before=["decision_agent"])
# Auto-reject gates
def route_post_compliance(state):
if state.fraud_risk_score > 0.85:
return "auto_reject"
if "CRITICAL" in state.compliance_flags:
return "auto_reject"
return "decision_agent"
```
---
## ML Models
### Credit Scoring
- **Features**: 11 engineered features (credit score, bureau score, DTI, bank balance, income stability, loan-to-income ratio, fraud risk)
- **Model**: XGBoost with Optuna 50-trial hyperparameter optimization
- **Class Imbalance**: SMOTE oversampling (default rate ~5%)
- **Explainability**: SHAP TreeExplainer — every decision has feature attributions
- **MLflow**: Experiment tracking + model registry + deployment gating (AUC quality gate)
### Fraud Detection
- **Model**: Isolation Forest (anomaly detection — no labelled fraud data required)
- **Rule Engine**: Income-bank ratio, DTI > 60%, loan > 5× annual income
- **Combined Score**: `min(1.0, rules_score + ml_score × 0.4)`
### Drift Monitoring
- **Evidently AI**: Weekly drift reports on credit features
- **Airflow DAG**: Auto-retrain on drift detection with AUC quality gate
---
## RBI Compliance
| Rule | Implementation |
|------|---------------|
| DTI ≤ 50% | Automated check in `compliance_checker.py` |
| KYC mandatory | Penny drop + Aadhaar OTP + PAN verification |
| PMLA ₹2L threshold | Automated suspicious transaction flag |
| Age eligibility | Min 18, max varies by loan type |
| Fair lending | No discrimination on caste/religion/gender |
| DPDP Act 2023 | PII masked in logs, consent tracked, 180-day retention |
| Audit trail | Immutable audit log in PostgreSQL, S3 backup |
| SHAP explanation | Regulator-ready decision explanation for every loan |
---
## API Reference
### `POST /api/v1/loans/underwrite`
```json
{
"applicant_name": "Ajay Mahale",
"pan_number": "ABCPM1234A",
"loan_type": "personal",
"loan_amount": 500000,
"tenure_months": 36,
"monthly_income": 80000,
"account_number": "123456789012",
"ifsc_code": "HDFC0001234"
}
```
**Response:**
```json
{
"application_id": "APP-20260501-001",
"decision": "APPROVED",
"approved_amount": 500000,
"interest_rate": 11.5,
"credit_score": 0.78,
"risk_tier": "NEAR_PRIME",
"fraud_risk_score": 0.08,
"shap_values": { "credit_score": 0.15, "dti_ratio": -0.04, ... },
"decision_explanation": "Application approved. Strong income stability...",
"rbi_compliant": true,
"processing_time_ms": 2840
}
```
---
## Running Tests
```bash
make test # All tests
make test-agents # Agent unit tests
make test-ml # ML model tests
make test-mcp # MCP server tests
make test-cov # With coverage report (target: 70%+)
```
---
## Deployment
```bash
# Terraform (AWS Mumbai ap-south-1)
cd infra/terraform
terraform init
terraform plan
terraform apply # Creates: EKS, RDS, ElastiCache, MSK Kafka, S3 (KMS encrypted)
# CI/CD: GitHub Actions on push to main
# → Tests → Security scan (Trivy) → Build ECR image → Deploy to EKS
```
---
## Resume Bullet Points 🚀
Use these in your Naukri / LinkedIn profile:
- **Architected** production agentic AI underwriting platform using LangGraph v0.3 with 6 parallel agents, reducing loan decision time from 3 days to under 5 seconds
- **Built** 5 MCP (Model Context Protocol) servers for real-time bureau, GST, Account Aggregator, and KYC integrations using FastMCP + Streamable HTTP transport
- **Implemented** XGBoost credit scoring model with SHAP explainability, Optuna HPO, and SMOTE class balancing; deployed via MLflow model registry with AUC quality gates
- **Engineered** DPDP Act 2023 and RBI Master Direction compliance layer with automated DTI, KYC, and PMLA checks; full audit trail in PostgreSQL
- **Deployed** on AWS EKS (Mumbai) with Terraform, MSK Kafka, encrypted RDS multi-AZ, and GitHub Actions CI/CD including Trivy security scanning
- **Integrated** Surya OCR for Hindi + English document extraction, Evidently AI for model drift monitoring, and Airflow for weekly automated model retraining
---
## Folder Structure
```
bfsi-credit-intelligence/
├── backend/
│ ├── agents/ # 6 LangGraph agents
│ ├── ml/ # XGBoost, Isolation Forest, SHAP
│ ├── mcp_servers/ # 5 MCP servers (Bureau, BankTxn, GST, RBI, PennyDrop)
│ ├── document_processing/ # OCR pipeline (PyMuPDF + Surya)
│ ├── streaming/ # Kafka producer + consumer
│ └── main.py # FastAPI app
├── frontend/ # Next.js 14 underwriter dashboard
├── mlops/airflow/ # Weekly model retrain DAGs
├── infra/
│ ├── k8s/ # EKS manifests + HPA
│ ├── terraform/ # AWS infrastructure (ap-south-1)
│ └── docker/ # Multi-stage Dockerfiles
├── tests/ # pytest suite (agents, ML, MCP, API)
├── docker-compose.yml # Full local stack
└── Makefile # All dev commands
```