Multi-class Prediction for Credit Scoring: A Machine Learning Solution Based on Random Forest and CatBoost

This project focuses on the multi-class credit scoring problem, using Random Forest and CatBoost algorithms to build prediction models that classify customers into different credit levels. It aims to provide financial institutions with more accurate risk assessment tools. The project balances prediction accuracy and interpretability, and is applicable to various financial business scenarios such as loan approval and dynamic pricing.

Credit scoring is a core infrastructure in the financial industry, directly affecting key business decisions such as loan approval, credit card limits, and insurance pricing. Traditional methods rely on expert experience and simple statistical rules, making it difficult to capture complex non-linear relationships; machine learning analyzes massive historical data to automatically learn key factors affecting credit risk, bringing new possibilities to credit scoring.

• Random Forest: Strong anti-overfitting ability, can evaluate feature importance, supports mixed data types, and is robust to outliers
• CatBoost: Natively supports categorical features, ordered boosting reduces prediction bias, fast training speed, and built-in missing value handling

Multi-class Framework: Classify customers into four credit levels: Excellent, Good, Average, and Poor; address data imbalance using sampling, class weights, and evaluation metrics like F1-score/AUC; calibrate probabilities via Platt scaling or isotonic regression. Feature Engineering: Covers traditional features such as demographics, credit history, and debt status; constructs derived features like ratios, trends, and aggregations; uses filtering, wrapper, and embedding methods for feature selection.

• Time series validation: Split training and test sets in chronological order to avoid data leakage
• Cross-period stability test: Validate model adaptability on data from different time periods
• Fairness assessment: Check the distribution of prediction errors across different gender, race, and age groups to ensure model fairness

• Precision marketing: Targeted promotion of high-quality products to high-credit customers
• Dynamic pricing: Implement differentiated interest rates based on credit levels
• Credit limit management: Initial credit granting and dynamic adjustment mechanisms
• Collection strategy: Adopt differentiated collection methods for customers of different credit levels

Deployment and Monitoring: Deploy the model as an API service to support real-time scoring; continuously monitor model drift and trigger alerts; provide score explanations to meet regulatory requirements. Challenge Response: Resolve data quality issues through cleaning and labeling; regularly evaluate and update feature stability; ensure privacy compliance through desensitization, access control, and other measures.

This project demonstrates a typical application of machine learning in the financial risk control field, building a credit assessment system that balances accuracy and interpretability through the combination of Random Forest and CatBoost. It is an important component of digital transformation for financial institutions and an excellent case for data science learners to understand classification problems and tabular data processing.