Prediction of Car Loan Default Results Based on Multi Model Fusion

Zhaoyang Hong; Wenxuan Deng; Xiuyu Gong

doi:10.54097/fbem.v5i1.1515

Authors

Zhaoyang Hong
Wenxuan Deng
Xiuyu Gong

DOI:

https://doi.org/10.54097/fbem.v5i1.1515

Keywords:

Loan default, Multi-model fusion, Credit default, Credit risk, Machine learning.

Abstract

With the prosperity and development of the asset management industry and various financial derivatives, many micro-loans and online loans have gradually entered the public view. How to predict the default probability of customer loans is a hot topic in the market. Therefore, in this paper, by collecting the data profile of more than 10 thousand car loan borrowers and fitting the fusion model of 4 methods: logistic model, decision model, Random Forest, and KNN model to the data, the author examines the behavioral data of borrowers to predict whether the borrowers will default in the future and find the best threshold to reach the lowest cost. The findings indicate that our final prediction can reduce costs by 38.9%. The excellent result shows that this model can be applied to the real market to help lending institutions predict default results and formulate strategies to avoid default risks in the process of borrowers' evaluation according to the model coefficient.

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