timothynn/fairlend-kenya

# FairLend Kenya: Synthetic Data for Inclusive Credit Models ## 🎯 Problem Statement Traditional credit scoring in Kenya relies on historical data that often reflects societal biases, leading to discriminatory lending practices against women, rural applicants, and MSMEs in the informal sector. ## 💡 Our Solution FairLend uses Generative AI to create synthetic financial datasets that preserve credit risk patterns while eliminating discriminatory biases. This enables financial institutions to train fairer, more inclusive credit risk models. ## 🚀 Key Features - **Bias Detection & Auditing**: Comprehensive analysis of existing credit data for disparate impact - **Synthetic Data Generation**: Generative AI models that create fair, representative datasets - **Fairness Validation**: Metrics and tools to validate model fairness and performance - **Kenyan Context**: Tailored for Kenya's unique financial landscape (M-Pesa, SACCOs, informal sector) ## Repository Structure fairlend-kenya/ │ ├── README.md # Project overview & setup instructions ├── requirements.txt # Python dependencies ├── .gitignore ├── LICENSE # MIT or Apache 2.0 │ ├── data/ │ ├── raw/ # Original data (if allowed) │ ├── processed/ # Cleaned data │ ├── synthetic/ # Generated fair datasets │ └── sample_data.csv # Mock data for demo │ ├── src/ │ ├── __init__.py │ ├── data_processing/ │ │ ├── __init__.py │ │ ├── bias_detector.py │ │ └── data_cleaner.py │ ├── synthetic_generator/ │ │ ├── __init__.py │ │ ├── fair_gan.py │ │ └── data_validator.py │ └── visualization/ │ ├── __init__.py │ └── bias_dashboard.py │ ├── notebooks/ │ ├── 01_data_exploration.ipynb │ ├── 02_bias_analysis.ipynb │ ├── 03_model_training.ipynb │ └── 04_synthetic_generation.ipynb │ ├── tests/ │ ├── __init__.py │ ├── test_bias_detection …