Research on the Construction of Personal Credit Score Card Model based on Logistic Regression and Random Forest Fusion Model

Authors

  • Liying Yan
  • Yukai Ma
  • Yao Ming

DOI:

https://doi.org/10.54691/esd7m193

Keywords:

Personal Credit Score Card; Logistic Regression; Xgboost Model; Random Forest.

Abstract

This paper discusses the indicators and models of personal credit score cards, aiming to improve the accuracy and efficiency of evaluation. Through the literature review, we compare the traditional and machine-learning, deep learning-based scoring models, and focus on the combined prediction models to cope with the complex economic situations. Data processing was performed using random forest predicting missing data and XGBoost feature weights assisted feature selection. The model constructed a combined model of random forest and logistic regression and a credit score card assisted by the XGBoost model, which proved that the prediction effect of the combined model was better than that of a single model.

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References

Zhang Junli, Guo Shuangyan, Ren Cuiping, Ma Qian. Study on personal credit score card model based on logistic regression [J]. Modern Information Technology, 202,8 (05): 12-16.

Durand D.Risk Elements in consumer Installment financing[J].National Bureau of Economic Research, 1941:60~72.

Cui Boyu. A Credit Risk Assessment Model and Applied Research Based on Cost-Sensitive and Incremental Learning [D]. Southwestern University of Finance and Economics, 2022.

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Published

2024-07-26

Issue

Section

Articles

How to Cite

Yan, Liying, Yukai Ma, and Yao Ming. 2024. “Research on the Construction of Personal Credit Score Card Model Based on Logistic Regression and Random Forest Fusion Model”. Scientific Journal of Intelligent Systems Research 6 (7): 34-39. https://doi.org/10.54691/esd7m193.