Outline:
This project implements an AI-powered credit underwriting system that leverages machine learning (ML) and reinforcement learning (RL) to optimize loan approval decisions while managing risk. It includes:
(i) ML-Based Credit Risk Prediction (Random Forest)
(ii) Reinforcement Learning Agents (PPO & DQN) for dynamic decision-making
(iii) FastAPI Server for real-time loan application processing
(iv) Risk-Aware Decision Model for enhanced financial risk management
a) ML Model (Credit Scoring)
- Algorithm: Random Forest
- Features Used: Credit Score, Income, Debt-to-Income Ratio, Age, Employment Years, Loan Amount
- Output: Approval Decision (1 = Approved, 0 = Rejected)
b) Reinforcement Learning Agents
- PPO (Proximal Policy Optimization) → Focuses on optimizing long-term rewards
- DQN (Deep Q-Networks) → Handles risk control in loan approvals
- Custom OpenAI Gym Environment simulates credit applications
c) Risk-Aware Decision Policy
- Combines ML & RL to make more informed approval decisions
- Incorporates Risk Factors such as loan amount & interest rates
- Prevents High-Risk Lending through reinforcement learning penalties
After training the models, start the API: uvicorn api:app --reload
Future Enhancements
✅ Expand dataset with real-world financial data
✅ Improve model interpretability with SHAP values
✅ Deploy on AWS/GCP with real-time transaction processing
Tech Stack:
- ML: Scikit-Learn (Random Forest)
- RL: Stable-Baselines3 (PPO, DQN)
- API: FastAPI
- Backtesting & Simulation: OpenAI Gym
🚀 Ready to transform credit underwriting with AI? Let's go! 🎯