Credit Risk Assessment

Overview

The Credit Risk Assessment project is designed to evaluate and predict the likelihood of loan default by leveraging machine learning techniques. It assesses borrower creditworthiness, providing insights that help financial institutions manage risk effectively. The model predicts key financial metrics such as Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). These predictions support the calculation of Expected Loss (EL) and inform lending decisions that optimize profitability and mitigate risks.

Key Objectives

1. Probability of Default (PD): Estimate the likelihood that a borrower will default on a loan.
2. Loss Given Default (LGD): Determine the proportion of the loan that would be lost if default occurs.
3. Exposure at Default (EAD): Assess the outstanding loan balance at the time of default.
4. Expected Loss (EL): Combine PD, LGD, and EAD to calculate the anticipated financial loss.

Technical Implementation

Data Preprocessing

• Data Cleaning: Address missing values and outliers.
• Feature Engineering: Create new features, apply one-hot encoding for categorical variables, and perform scaling.
• Data Splitting: Divide the dataset into training and testing sets to ensure robust model validation.

Model Development

• Logistic Regression: Used as a baseline model for predicting PD.
• Random Forest and Gradient Boosting: Employed for more advanced and accurate predictions of PD, LGD, and EAD.
• Custom Threshold Tuning: Applied to optimize the trade-off between precision and recall based on business needs.

Model Evaluation

• Performance Metrics: Evaluate models using AUC-ROC, Precision, Recall, F1-Score, and Confusion Matrix.
• Cross-Validation: Perform cross-validation to ensure model stability and generalization.
• Feature Importance Analysis: Identify the most impactful features driving the model's predictions.

Financial Impact and Business Insights

Profitability Analysis

• By estimating the financial risk associated with each loan, the model supports the determination of risk-adjusted interest rates. This ensures that loans remain profitable even when extended to higher-risk customers.

Risk Management

• The model's ability to predict PD, LGD, and EAD helps in constructing a well-balanced loan portfolio that minimizes potential losses while maximizing returns.

Customer Segmentation

• By analyzing customer profiles and risk factors, the model identifies segments with varying levels of risk, allowing for targeted marketing strategies and tailored financial products.

Financial Aspects

1. Enhanced Risk Management:

   • Default Prediction: Reduces default rates and stabilizes the balance sheet.
   • Capital Allocation: Optimizes risk-adjusted returns through expected loss predictions.
   • Portfolio Diversification: Aids in creating diversified loan portfolios.

2. Profitability Optimization:

   • Interest Rate Adjustment: Risk metrics integrated with pricing models.
   • Fee Structure: Designed to compensate for higher risks.
   • Customer Lifetime Value (CLV): Identifies low-risk borrowers for long-term engagement.

3. Regulatory Compliance and Reporting:

   • Basel III Compliance: Accurate RWA and ECL calculations.
   • Transparency: Supports better regulatory reporting and audits.
   • Stress Testing: Integrates with stress-testing frameworks for scenario analysis.

Long-Term Strategic Impact

1. Scalability: Modular design supports growth across regions and products.
2. Market Competitiveness: Lowers default rates, attracts more customers.
3. Customer Trust: Better credit offers enhance borrower loyalty.