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Research on credit risk assessment optimization based on machine learning
- 1 University of Michigan-Ann Arbor,Ann Arbor, MI, USA, 48109
- 2 Pepperdine University, 24255 Pacific Coast Hwy, Malibu, CA 90263
- 3 Columbia University, New York, 10027, USA
- 4 Peking University, Peking China, 100091
* Author to whom correspondence should be addressed.
Abstract
Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.
Keywords
Credit Risk Assessment, Machine Learning, Random Forest, Correlation Analysis, Optimization
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Cite this article
Zhang,X.;Xu,L.;Li,N.;Zou,J. (2024). Research on credit risk assessment optimization based on machine learning. Applied and Computational Engineering,69,173-178.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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