Credit Card Default Prediction: Practical Application of Data Mining Technology in Financial Risk Control

This article deeply analyzes a complete machine learning project for credit card default prediction, covering the entire process from data preprocessing to model deployment. It focuses on discussing the method of using SMOTE oversampling technology to handle class imbalance issues, as well as the performance comparison and tuning strategies of logistic regression, random forests, and multi-layer perceptrons in financial risk control scenarios, demonstrating the practical application value of data mining technology in financial risk control.

Intelligent Transformation of Financial Risk Control

Credit card business is a core revenue source for banks, but credit risk is concentrated, and the default rate may exceed 5% during economic fluctuations. Traditional manual review and simple scorecards are difficult to handle massive applications and complex fraud. Machine learning can analyze customer behavior, demographic, and transaction data to achieve millisecond-level default probability assessment, promoting the automation of risk control.

Dataset Overview

The project uses the UCI Taiwan Bank dataset of 30,000 customers, including 24 features (demographics, credit history, repayment behavior) and 1 binary target (whether defaulted). The data has severe class imbalance: defaulting customers account for only 22.12%, while normal customers account for 77.88%. If not handled, the model tends to predict normal, losing the ability to identify risks.

Data Preprocessing

• Missing value handling: Mode imputation for missing education level and marital status (missing ratio <5%); IQR method to identify and truncate outliers in numerical features.
• Feature encoding: One-hot encoding for categorical variables (gender, education level, etc.) to avoid false ordinal relationships.
• Feature scaling: Standardize numerical features (mean 0, standard deviation 1) to eliminate dimensionality effects.

SMOTE Oversampling

To address class imbalance, synthesize minority class samples in the training set: for each default sample, find k nearest neighbors, randomly generate synthetic samples along the line between the sample and its neighbors, expand the number of default samples to the same as normal samples (23,364), while keeping the original distribution in the validation/test sets.

Model Selection and Training

• Logistic regression: L2 regularization to prevent overfitting, grid search to optimize regularization strength; advantage is strong interpretability.
• Random forest: Integrate multiple decision trees, tune parameters like number of trees and maximum depth; nonlinear modeling ability is better than logistic regression.
• Multi-layer Perceptron (MLP): Two hidden layers, ReLU activation + Adam optimization, early stopping strategy to prevent overfitting.

Hyperparameter Tuning

Grid search combined with stratified k-fold cross-validation (maintaining consistent default ratio in each fold), parallel computing to speed up, select the optimal hyperparameter combination based on the validation set.

In class imbalance scenarios, accuracy has no reference value; multiple metrics are used for evaluation:

• Confusion matrix: Focus on recall rate (proportion of actual defaults correctly identified), as the cost of missed detection is much higher than false positives.
• ROC curve and AUC: Random forest has an AUC of about 0.82, with the best ability to distinguish positive and negative samples.
• PR curve: Shows the trade-off between precision and recall at different thresholds, supporting flexible adjustment of approval strategies for business.
• Cost-sensitive learning: Assign higher penalties to false negative errors to improve risk identification ability.

This project demonstrates a typical application paradigm of machine learning in financial risk control: from data understanding to feature engineering, from model training to business deployment, it requires a combination of technology and domain knowledge. SMOTE successfully solves class imbalance, multi-model comparison provides a basis for algorithm selection, and comprehensive evaluation ensures model practicality. With the development of RegTech and open banking, intelligent risk control will become the core competitiveness of financial institutions, which is an inevitable path for the digital transformation of the industry.

Business Deployment Considerations

• Real-time inference: Logistic regression and lightweight random forests meet millisecond-level approval requirements.
• Model monitoring: Establish a dashboard to track prediction distribution and actual default rate; retrain when performance drops beyond the threshold.
• Fairness review: Regularly audit model performance differences across different groups (gender, age, etc.) to avoid implicit bias.
• Interpretability: Use logistic regression or SHAP technology to meet regulatory interpretation requirements.

Future Improvement Directions

• Verify the generalization ability of the model in other regions.
• Construct derived features (e.g., repayment ratio, credit limit usage trend).
• Introduce time-series models (RNN/TCN) to capture the dynamic evolution of customer behavior.
• Integrate external data sources (credit reporting, social media).
• Try gradient boosting frameworks like XGBoost/LightGBM.
• Develop online learning mechanisms to achieve continuous model updates.