Machine LearningJuly 23, 20232 min read

Predicting Credit Default (Lending Club)

Problem Statement

Peer-to-peer lending platforms approve thousands of loans daily with limited underwriting infrastructure. Using 38,971 historical loans from Lending Club (2007–2011), this project benchmarks multiple classification algorithms to predict which borrowers will default - and quantifies just how difficult consumer credit modeling remains even with 38 attributes.

Methodology

Data & Preprocessing

Our analysis uses data from 2007-2011, comprising 38,971 observations and 38 attributes. Four categories of models were developed to predict loan status:

Logistic Regression (Backwards Selection)
LASSO model
Elastic Net model
Random Forest model

Model Training Pipeline

The model training process involved four steps:

Choosing performance metrics: RMSE, MAE, and R-squared
Benchmarking with 7 different algorithms (Linear Regression, KNeighbors, AdaBoost, etc.)
Fine-tuning the 2 best models using k-fold cross validation
Exporting the best model for application on the test set

Results

Distribution of loan status showing class proportions — Distribution of loan outcomes in the Lending Club dataset.

Key predictor variables for credit default — Key predictor variables identified through exploratory data analysis.

ROC curve showing 69% AUC for the final model — ROC curve for the final model. Validation AUC of 69%, highlighting the inherent difficulty of consumer credit modeling.

Loan amount and interest rate distributions — Distribution of loan amounts and interest rates reveals lending patterns and risk tiers.

The final validation AUC of 69% underscores the challenge of consumer credit default prediction - even with 38 attributes, borrower behavior remains difficult to model with high accuracy.

Next Steps

Engineer additional features from existing attributes to capture non-linear relationships
Explore gradient boosting methods (XGBoost, LightGBM) for potentially better performance
Incorporate temporal features to capture economic cycle effects on default rates