Customer Credit Risk Prediction and Identification

Sun, 01 Jan 2023 00:00:00 +0000

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

Data Overview:
- Combined datasets include 24,983 samples and 205 features. After preprocessing, 124 features remain (33 textual and ~33% date-related features).
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.
Feature Selection and Engineering:
- Importance-ranked features selected via XGBoost.
- Featuretools used to generate new feature combinations.

Model Building

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.
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

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.
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.