💳 Credit Risk Intelligence System (Detection Rate 91.5%)

Alternate Credit Scoring System for Unbanked Users Using Dynamic Threshold Optimization.

An advanced machine learning solution that predicts credit default risk for unbanked and thin-file users using the Home Credit dataset (307,511 records). Optimized for Portfolio Profitability, this system maximizes the "Net Financial Benefit" through synchronized feature parity between research and production.

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🎯 Problem Statement

• Objective: Predict credit default risk for unbanked users
• Dataset: 307,511 loan applications from Home Credit
• Challenge: Severe class imbalance (only 8% defaults vs 92% repaid)
• Goal: Build cost-optimized models that minimize financial losses while maximizing default detection

Data Overview

• Total Records: 307,511 (Training)
• Class Distribution:
  • Non-defaulters: 282,686 (91.9%)
  • Defaulters: 24,825 (8.1%)
  • Imbalance Ratio: 11.39:1

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🔍 Solution Approach: Profit-First ML

This project implements a Synchronized Pipeline where the research environment dynamically calibrates the production environment.

Model Evolution & Strategy

1. 🏆 Random Forest (Winner): 200 trees with class_weight='balanced'. Optimized for the highest Net Financial Benefit.
2. LightGBM + SMOTE: Gradient boosting with synthetic oversampling for aggressive risk capture.
3. XGBoost (Cost-Sensitive): Advanced ensemble using scale_pos_weight for loss minimization.

Key Techniques Applied

• ✅ 125-Feature Engineering: 13 unique credit-specific metrics, including DAYS_EMPLOYED_PERCENT.
• ✅ Dynamic Calibration: Automated calculation of Loss per Default ($513k) and Opportunity Cost ($30k).
• ✅ Robust Preprocessing: Manual RobustScaler implementation using synchronized calibration_params.json.
• ✅ Threshold Optimization: Searching the Global Maximum for Portfolio Utility (Winner at 0.40).
• ✅ SHAP Explainability: 1D feature impact analysis for regulatory reason-codes.

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📊 FEATURE ENGINEERING & SCALING

Preprocessing Steps

1. Outlier Detection: IQR clipping with 3.0× tolerance on 8 numeric columns
2. Missing Values: Median imputation for numeric features
3. Categorical Encoding: LabelEncoder + one-hot encoding for categorical features
4. Feature Engineering: 13 credit-specific features created

Critical Domain Features Created

• DAYS_EMPLOYED_PERCENT - Employment duration relative to age (125th Feature)
• CREDIT_INCOME_RATIO    - Total loan amount relative to annual earnings
• ANNUITY_INCOME_RATIO   - Monthly payment sustainability
• EXT_SOURCE_AVG         - Aggregated agency scores

Feature Parity Summary

Phase	Features	Status
Raw Data	122	Baseline
Post-Engineering	140	Expanded
Final Production	125	Optimized (Deduplicated)

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🤖 MODEL PERFORMANCE COMPARISON

Financial Optimization Logic

Models were optimized to minimize the Total Portfolio Loss:

• Loss Per Default: Median AMT_CREDIT of defaulters (~$513k).
• Opportunity Cost: 10% of the median annual annuity for good clients (~$30k).

🥇 FINAL RESULTS (Ranked by Net Benefit)

1️⃣ Random Forest (PRODUCTION WINNER) ⭐⭐⭐

• ROC-AUC: 0.8866
• Recall (Detection): 91.46%
• Optimal Threshold: 0.40
• Net Financial Benefit: ~$9.08 Billion 💰

2️⃣ LightGBM + SMOTE ⭐⭐

• ROC-AUC: 0.7813
• Detection Rate: 89.73%
• Optimal Threshold: 0.05
• Net Benefit: ~$6.77 Billion

3️⃣ XGBoost (Cost-Sensitive) ⭐

• ROC-AUC: 0.7647
• Detection Rate: 81.9%
• Optimal Threshold: 0.23
• Net Benefit: ~$6.63 Billion

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📂 Project Structure

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├── app.py                # Asynchronous FastAPI Inference Engine
├── nb/
│   └── notebook.ipynb    # Main Analysis, Calibration & Research
├── datasets/
│   └── test.csv          # Evaluation & Bulk Lookup Dataset
├── models/
│   ├── random_forest_model.joblib  # Production Model Binary
│   ├── feature_names.json          # 125-feature schema
│   └── calibration_params.json     # RobustScaler Statistics (Sync'd)
├── static/               # Premium Dark-Mode Dashboard
│   ├── index.html        # UI Skeleton (Glassmorphism)
│   ├── script.js         # Frontend Logic (SHAP Integration)
│   └── style.css         # UI Aesthetics
├── DEPLOYMENT_GUIDE.md    # Installation & Calibration Instructions
├── ARCHITECTURE.md        # Technical Design & Data Flow
├── requirements.txt       # Python Dependencies
└── history.db             # SQLite Production Audit Log

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🚀 QUICK START GUIDE

1. Installation

# Clone and enter directory
pip install -r requirements.txt

2. Calibration (Ensuring Parity)

Open nb/notebook.ipynb and Run All. This will:

• Calibrate the RobustScaler on the training set.
• Find the most profitable threshold.
• Export the latest calibration_params.json.

3. Launch the Dashboard

python -m uvicorn app:app --reload --host 0.0.0.0 --port 8005

Access the dashboard at http://localhost:8005.

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💡 Key Technical Pillars

1. Feature Synchronization: The 125-feature engineering in app.py is an exact manual replica of the notebook.ipynb logic to ensure zero prediction drift.
2. Outlier Resistance: By using RobustScaler (Median/IQR), the model remains stable even when applicants report extremely high or low incomes.
3. AI Explainability: Every prediction returns the top 6 SHAP factors, explaining why a loan was approved or rejected (e.g., "High income decreases risk", "Low agency scores increase risk").

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Last Updated: April 22, 2026
Author: Shivam Maurya