💸 Credit Underwriting Hackathon – Teen Titans Submission

Welcome to our submission for the Credit Underwriting Innovation Hackathon. Our system is designed to estimate income and repayment capability of financially underserved individuals using non-banking, privacy-compliant data sources. Built with an emphasis on modularity, explainability, and real-world deployability.

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🚀 Project Overview

🎯 Objective

To predict an individual’s creditworthiness in the absence of formal income declarations by leveraging publicly available features and constructing a two-stage machine learning model.

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🧠 Solution Architecture

🔹 Stage 1: EMI to Income Proxy Estimator

• Goal: Predict emi_to_income as a proxy for income using non-sensitive, publicly observable features.
• Model: CatBoostRegressor trained on synthetic EMI and alternate data.
• Input Features: Region, employment patterns, loan counts, balance-to-limit ratios, etc.

🔹 Stage 2: Final Income Predictor

• Goal: Use predicted emi_to_income + engineered financial features to predict target_income.
• Model: CatBoostRegressor fine-tuned on aligned features with robust validation.
• Safety: No use of personally identifiable or directly declared income data.

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🔧 Setup Instructions

🖥️ Environment Setup

Install dependencies using:

pip install -r requirements.txt

🏦 Credit Underwriting Model

A machine learning pipeline to estimate EMI-to-Income ratio and underwrite credit applicants using bureau data.
Developed during [Hackathon Name] by Team: Teen Titans
Lead Developer & Model Architect: Kanishka Kumar Singh

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📁 Directory Structure

Credit_UnderWriting_Model/
├── data/
│ ├── bureau_data_10000_without_target.csv
│ ├── participant_col_mapping.csv
├── outputs/
│ └── final_predictions.csv
├── models/
│ ├── catboost_model.pkl
│ └── emi_to_income_proxy_model.pkl
├── scripts/
│ ├── train_proxy_model.py
│ ├── train_main_model.py
│ └── run_inference.py
├── requirements.txt
└── README.md

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📊 Evaluation Metrics

Model Stage	Metric	Value
Proxy Model	RMSE	~0.00003
Final Model	R² Score	~0.68–0.82 (varied)

⚠️ Note: Accuracy degraded on blind test due to schema drift and noisy categorical encodings.

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🧩 Key Features Engineered

• debt_to_credit: Ratio of outstanding debt to credit limit.
• pin_region: First two digits of pin code to map regional economics.
• count_features: Total active accounts, limits, balances.

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🔍 Explainability

Used SHAP (SHapley Additive exPlanations) for interpreting model predictions.

import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_sample)
shap.summary_plot(shap_values, X_sample)

🧪 Run Inference

To generate predictions:

python scripts/run_inference.py

Steps performed:

• Loads test CSV and applies preprocessing
• Predicts emi_to_income using proxy model
• Applies imputations and final prediction
• Saves output to outputs/final_predictions.csv

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⚖️ Ethical Compliance

• ✅ No personal income data used
• ✅ No user-identifiable information
• ✅ Focused on transparency and fairness

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👨‍💻 Team

• Kanishka Kumar Singh – Lead ML Developer & Model Architect
• Varun Kant - Frontend Developer
• Pranjal Agarwal - Lead Developer
• Naman Jaju - Backend Developer

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🙏 Acknowledgements

• Organized by: [LenDenClub]
• Dataset provided by: [LenDenCLub]
• Special thanks to mentors, professors, and open-source contributors ❤️

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📌 Lessons Learned

• Data quality > model complexity
• Pipeline alignment & categorical consistency are critical
• SHAP is 🔥 for debugging blind spots
• And most importantly… sleep is underrated 😴

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📎 Appendix

• All required packages listed in requirements.txt
  (e.g., CatBoost, SHAP, pandas, NumPy, etc.)
• Trained models saved using joblib format in the models/ directory