Credit Risk Modelling

What is Credit Risk?

Credit risk is the risk that a borrower will fail to meet their financial obligations (e.g., fail to repay a loan), resulting in a loss for the lender (e.g., a bank).

🧮 What is Credit Risk Modelling?

Credit Risk Modelling refers to the use of statistical, mathematical, and machine learning techniques to estimate the likelihood of default and the potential loss in the event of default.

📌 It is a core part of risk management for banks, microfinance institutions, fintechs, and credit-rating agencies.

🎯 Objectives of Credit Risk Modelling:

Evaluate a borrower's creditworthiness
Make data-driven lending decisions
Minimize non-performing loans (NPLs)
Support capital allocation and regulatory compliance (e.g., Basel II/III, IFRS 9)

🧱 Key Components of Credit Risk Models:

🔢 Probability of Default (PD)

The chance that a borrower will default within a specific time frame (usually 12 months).
💸 Loss Given Default (LGD)

The portion of the loan the lender loses if the borrower defaults (after recovering collateral, etc.).
📆 Exposure at Default (EAD)

The amount the borrower owes at the moment of default.

💡 Expected Loss (EL) = PD × LGD × EAD

🧠 Common Credit Risk Models:

Model	Description
Logistic Regression	Standard model for default prediction (binary outcome: default / no default)
Decision Trees / Random Forest	Tree-based models that handle nonlinearities and feature interactions
Gradient Boosting (XGBoost, LightGBM)	Powerful ensemble methods often used in production
Neural Networks	Used for complex, large-scale risk prediction
Survival Analysis	Predicts time until default
Altman Z-Score	Bankruptcy prediction for corporations

📊 Metrics for Model Evaluation:

Metric	Purpose
Accuracy	Overall prediction correctness
AUC-ROC	Discrimination between defaulters and non-defaulters
Precision/Recall	Measures false positives/negatives
Gini Coefficient	Used in scorecard models
Confusion Matrix	Visualizes prediction errors

📁 Typical Data Used:

Age, gender, marital status
Credit history (past defaults, delinquencies)
Income and expenses
Employment type and duration
Education level
Collateral info
Bank transactions, utility payments, phone usage (alt-data)

🏦 Practical Applications:

📋 Credit scoring for personal loans, mortgages, BNPL, etc.
💼 Corporate lending risk assessments
📈 Portfolio-level risk management
📊 IFRS 9 provisioning (expected credit losses)
✅ Compliance with Basel II/III/IV

✅ Benefits of Strong Credit Risk Modelling:

Fewer bad loans and write-offs
Faster and more accurate decision-making
Improved capital planning and provisioning
Enhanced regulatory compliance
Better customer segmentation