Default Risk Analysis

Methodology

This project followed a comprehensive data science workflow:

Data Exploration and Preprocessing

The analysis began with thorough exploration of the loan data to understand its structure and characteristics:

• The dataset contained financial metrics, credit history information, and borrower details
• Initial inspection revealed no missing values in the dataset
• Duplicate entries were identified and evaluated
• Several categorical variables were properly encoded, including BankruptcyHistory, PreviousLoanDefaults, MultipleIncomeSources, and StudentLoanDebt

Feature Engineering

To improve model performance and handle data characteristics:

• Log Transformations: Applied to highly skewed financial variables to normalize their distributions:
  • AnnualIncome, SavingsAccountBalance, CheckingAccountBalance, TotalAssets
  • TotalLiabilities, MonthlyIncome, NetWorth, MonthlyLoanPayment
  • SecuredDebt, UnsecuredDebt
• Outlier Analysis: Comprehensive outlier detection was performed using IQR method to identify potential anomalies in both original and transformed features.
• Feature Selection: To address multicollinearity and simplify the model:
  • Dropped Annual Income in favor of Monthly Income due to high correlation
  • Removed Age in favor of Work Experience
  • Eliminated Total Liabilities as it was a direct sum of Secured and Unsecured Debt
  • Removed highly correlated financial metrics to prevent redundancy

Modeling Approach

A systematic approach was employed to build and evaluate multiple models:

• Data Splitting: The dataset was split into training (75%) and testing (25%) sets with stratification to ensure representative sampling
• Feature Preprocessing:
  • Numerical features: Missing values (if any) imputed with mean and standardized
  • Categorical features: Transformed using one-hot encoding
• Model Evaluation: Multiple regression models were trained and evaluated:
  • Linear Regression, Support Vector Regression (SVR)
  • Decision Tree, Random Forest, AdaBoost, Gradient Boosting
  • Neural Networks (MLPRegressor), XGBoost, LightGBM
• Hyperparameter Tuning: Grid search cross-validation was used to optimize the XGBoost model parameters:
  • n_estimators
  • max_depth
  • learning_rate: [0.1, 0.01, 0.001]

Key Findings & Results

Model Performance Comparison

After training multiple regression models, performance metrics (RMSE, MAE, and R²) were calculated to identify the most effective approach. Based on the evaluation metrics in the code, tree-based ensemble models consistently outperformed simpler models.

Best Performing Model

The XGBoost regressor emerged as the optimal model after hyperparameter tuning. The best configuration included:

• learning_rate: 0.1
• max_depth: 3
• n_estimators: 3001

This optimized model demonstrated the best balance of predictive accuracy and generalization capability when evaluated against the test data.

Feature Importance Analysis

SHAP (SHapley Additive exPlanations) values were used to understand feature contributions to the risk scores. The top influencing factors identified were:

• Financial metrics: Variables related to debt levels and income showed significant influence on risk scores
• Credit history: Previous default history and bankruptcy information were strong predictors
• Asset information: Account balances and net worth provided valuable signals

The SHAP analysis revealed both the magnitude and direction of each feature's impact, providing interpretable insights beyond traditional feature importance methods.

Risk Segmentation

The analysis revealed distinct borrower segments with different risk profiles:

• Low-Risk Segment: High income, excellent credit history, low debt-to-income ratio
• Medium-Risk Segment: Average credit scores, moderate debt levels, stable employment
• High-Risk Segment: Low credit scores, high debt-to-income ratios, unstable income
• Special Consideration Segment: Good income but limited credit history

Recommendations

Based on the model results and data analysis, the following recommendations were developed:

Strategic Implementation

• Early Intervention Protocol:
  • Develop a tiered intervention system based on the risk score thresholds
  • Create specialized outreach strategies for different risk segments
  • Implement continuous monitoring of high-impact variables identified in the model
• Model Integration:
  • Deploy the trained XGBoost model into existing loan servicing systems
  • Create dashboards for product managers to monitor portfolio risk levels
  • Establish automated alerts when borrowers cross critical risk thresholds
• Ethical Safeguards:
  • Implement regular bias audits to ensure the model does not disproportionately impact protected classes
  • Develop supplementary models that exclude potentially problematic variables
  • Create transparent documentation explaining how risk scores are calculated and used

Future Enhancements

• Model Refinement:
  • Incorporate time-series analysis to detect evolving patterns in borrower behavior
  • Explore additional feature engineering techniques to further improve performance
  • Develop specialized models for different loan types or borrower segments
• Validation Framework:
  • Establish ongoing validation protocols to ensure model stability over time
  • Create A/B testing framework to measure the effectiveness of interventions
  • Develop feedback mechanisms to continuously improve the model based on real-world outcomes
• Knowledge Transfer:
  • Create training materials for servicing teams to properly interpret and act on risk scores
  • Develop clear guidelines on appropriate interventions based on risk levels
  • Establish governance procedures for model updates and modifications

By implementing these recommendations, lending institutions can leverage the insights from this analysis to improve portfolio performance while ensuring fair treatment of all borrowers.

Jupyter Notebook

The complete analysis, including code, visualizations, and detailed findings, is available in the embedded Jupyter notebook below.

Loading notebook... This may take a moment as the notebook is quite large (7MB).