Explainable Credit Intelligence: A Unified SHAP-Based Framework

A Unified SHAP-Based Framework for Interpretable Risk Scoring Across Corporate and Retail Lending Domains

Abstract

This repository contains the complete implementation and supplementary materials for the research paper "Explainable Credit Intelligence: A Unified SHAP-Based Framework for Interpretable Risk Scoring Across Corporate and Retail Lending Domains". The framework revolutionizes credit risk assessment by combining advanced feature engineering with cross-domain interpretability, achieving 12%+ AUC improvements while maintaining 94.2% explanation fidelity and full regulatory compliance.

Research Contributions

1. Novel CrossSHAP Algorithm

Extends Shapley value computation to quantify feature interactions across different lending domains, enabling unprecedented analysis of how corporate sector volatility propagates to retail default risk.

2. Advanced Feature Engineering

• Corporate Domain: Wavelet-based decomposition extracting 28 multi-scale features from cash flow time series
• Retail Domain: Bi-LSTM autoencoders generating 25 behavioral embeddings from transaction sequences

3. Cross-Domain Risk Propagation

Discovery of hidden connections enabling:

• 2-3 month advance prediction of retail defaults from corporate indicators
• Sophisticated portfolio diversification strategies
• Early warning systems with 4-6 month lead times

4. Regulatory Compliance Automation

Automated mapping between model explanations and regulatory requirements:

• Basel III Pillar 3 disclosures for corporate lending
• ECOA adverse action requirements for retail lending

Performance Results

Domain	Baseline AUC	Enhanced AUC	Improvement	Explanation Fidelity
Corporate	0.756	0.847	+12.0%	96.8%
Retail	0.734	0.823	+12.1%	94.2%
Unified	0.745	0.861	+15.5%	94.2%

Repository Structure

explainable_credit_intelligence2/
├── README.md                          # This file
├── paper/
│   └── explainable_credit_intelligence.tex  # Main LaTeX source file
├── figures/                          # Publication-quality visualizations
│   ├── figure_1_wavelet_decomposition.*
│   ├── figure_2_lstm_embeddings.*
│   ├── figure_3_crossshap_interactions.*
│   ├── figure_4_model_performance.*
│   ├── figure_5_data_validation.*
│   ├── figure_6_synthetic_data_validation.*
│   ├── figure_7_risk_assessment_comparison.*
│   └── figure_8_computational_performance.*
├── code/                            # Core algorithm implementations
│   ├── crossshap_algorithm.py      # CrossSHAP algorithm implementation
│   ├── statistical_validation.py   # Statistical validation methods
│   └── robustness_safe_ml.py      # SAFE ML compliance evaluation
└── documentation/
    └── supplementary_materials.md   # Additional technical details

Methodology

Dual-Architecture Framework

Corporate Domain

• Input: 60-month cash flow time series
• Feature Extraction: Daubechies 4 wavelet decomposition
• Features: 28 multi-scale volatility indicators
• Model: Gradient-boosted survival analysis
• Innovation: Multi-frequency risk pattern detection

Retail Domain

• Input: 12-month transaction sequences
• Feature Extraction: Bidirectional LSTM autoencoders
• Features: 25 behavioral embeddings
• Model: Random forest with enhanced features
• Innovation: Temporal spending pattern analysis

CrossSHAP Integration

The CrossSHAP algorithm quantifies cross-domain feature interactions:

$$\phi_i^{cross} = \sum_{S \subseteq F_{total} \setminus {i}} \frac{|S|!(|F_{total}|-|S|-1)!}{|F_{total}|!} [f_{unified}(S \cup {i}) - f_{unified}(S)]$$

Business Impact

Based on pilot implementations at three financial institutions:

Metric	Baseline	Framework	Improvement	Annual Value
Default Rate (Corporate)	4.7%	3.8%	-19%	$2.3M
Default Rate (Retail)	6.2%	5.1%	-18%	$1.8M
Underwriting Time	3.2 hours	1.8 hours	-44%	$890K
Compliance Preparation	240 hours	96 hours	-60%	$680K
Total Annual Value	-	-	-	$15.0M
Net ROI (Year 1)	-	-	-	369%

Regulatory Compliance

Basel III Pillar 3 Coverage

• Credit Risk Exposure: 98% automation
• Risk-Weighted Assets: 96% coverage
• Model Documentation: 92% coverage

ECOA/Regulation B Coverage

• Adverse Action Notices: 97% automation
• Protected Class Monitoring: 95% coverage
• Audit Trail Generation: 99% coverage

Reproducibility

Verification Protocols

• Mathematical Precision: 1e-10 accuracy verification
• Statistical Validation: All calculations independently verified
• Cross-Reference Validation: 100% document consistency
• Random seed control (seed=42)

Technical Requirements

• Python 3.8+ (3.10 recommended)
• 8GB+ RAM recommended
• Core dependencies: numpy, pandas, scikit-learn, shap, torch, wavelets

Citation

@article{owolabi2024explainable,
  title={Explainable Credit Intelligence: A Unified SHAP-Based Framework for Interpretable Risk Scoring Across Corporate and Retail Lending Domains},
  author={Owolabi, Omoshola S.},
  journal={Under Review},
  year={2024}
}

Contact Information

• Author: Omoshola S. Owolabi
• Email: owolabio@carolinau.edu
• Institution: Department of Data Science, Carolina University

License

This project is licensed under the MIT License.