cwawire/Credit-Risk-Modelling-Credit-Scoring

This project simulates how fintech lenders in Kenya assess borrower risk using limited but high-frequency data such as income, loan size, and mobile money activity. # Credit Risk Modelling & Credit Scoring ## Kenya Fintech Simulation --- ## Overview This project simulates how fintech lenders in Kenya assess borrower risk using limited but high-frequency data such as income, loan size, and mobile money activity. The goal is to: - Predict the likelihood of loan default - Convert predictions into a usable credit score (300–850) - Demonstrate how machine learning supports lending decisions in fintech environments --- ## Problem Statement In many fintech lending environments, especially in emerging markets, traditional credit history is often unavailable. As a result, lenders rely on: - Behavioral data (e.g., mobile money usage) - Income patterns - Loan history This project builds a model to estimate default risk and translate it into an actionable credit score. --- ## Dataset A synthetic dataset was created to simulate a Kenyan fintech environment, including: - **age** — Borrower age - **monthly_income** — Estimated monthly earnings - **loan_amount** — Requested loan amount - **mobile_txn_count** — Mobile money transaction frequency (proxy for financial activity) - **loan_history** — Number of past loans - **default** — Target variable (1 = default, 0 = non-default) --- ## Methodology ### 1. Data Simulation - Generated realistic borrower profiles using statistical distributions - Introduced relationships between income, loan size, and behavioral variables ### 2. Exploratory Data Analysis (EDA) - Examined feature distributions - Checked class balance - Analyzed correlations between variables ### 3. Feature Engineering - Derived risk-related features such as: - Loan-to-income ratio - Behavioral proxies for financial stability ### 4. Model Development A **Decision Tree Classifier** was used because: - It captures non-linear relationships - It is interpretable - It is commonly used in risk-based decision systems ### 5. Model Evaluation The model was evaluated using: - Precision & Recall - F1 Score - ROC-AUC T …