Customer Credit Risk Prediction and Identification

Competition: Third Sichuan University Financial Technology Modeling Competition Award: First Place **Awared by:**The Education Department of Sichuan Provincial

This project, presented for the Third Sichuan University Financial Technology Modeling Competition, focuses on customer credit risk prediction and identification. It emphasizes constructing a stable, high-performing binary classification model for credit risk management based on financial data.

Key Highlights

Data Analysis and Preparation

1. Data Overview:
   • Combined datasets include 24,983 samples and 205 features. After preprocessing, 124 features remain (33 textual and ~33% date-related features).
2. Data Cleaning:
   • Missing values:
     • Median imputation for continuous variables.
     • Mode imputation for discrete variables.
     • Filling with -99 for features with >95% missing.
   • Encoding methods:
     • Count encoding for categories with <10 values.
     • WOE binning for categories with >10 values.
   • Time features are extracted based on hours/minutes or intervals from the current day.
3. Feature Selection and Engineering:
   • Importance-ranked features selected via XGBoost.
   • Featuretools used to generate new feature combinations.

Model Building

1. Architecture:
   • Three-layered stacking framework:
     • Layer 1: Base models include CatBoost, LightGBM, XGBoost, and Random Forest.
     • Layer 2: Outputs from base models serve as inputs for four distinct sub-models.
     • Layer 3: Final predictions are generated through normalized weighted voting.
2. Model Optimization:
   • 5-fold cross-validation and grid search are applied to optimize hyperparameters for base models.

Evaluation

• Model performance is assessed using AUC (Area Under the Curve):
  • Individual Models:
    • CatBoost: 0.8486
    • LightGBM: 0.8476
    • XGBoost: 0.8464
    • Random Forest: 0.8423
  • Stacking and Voting:
    • Stacking 1: 0.8523
    • Stacking 2: 0.8530
    • Voting: 0.8594 (Best Performance)

Credit Rating System

1. Structure:
   • Customers are classified into 9 levels based on predicted risk, with clear distribution and distinguishable credit tiers.
   • 9.77% of users belong to the top levels (8 and above), reflecting the model’s discriminatory power.
2. Implementation:
   • Integrated with a web-based system using Docker and Vue.js for front-end services.
   • Compared to FICO models, the system provides zones for:
     • Quality customers.
     • Value exploration.
     • Overestimation.
     • Risk elimination.

Conclusions and Suggestions

• The project demonstrates strong modeling capabilities through effective stacking and feature engineering.
• The authors suggest refining the model for real-world applications and exploring the scalability of the approach for diverse datasets.

This project presents a robust credit risk modeling framework with promising performance, practical implications, and room for further enhancement in financial technology applications.