Data-Driven Credit Risk Assessment and Optimization Strategy Exploration

Lingyun Lai

doi:10.71222/br77ty76

Authors

Lingyun Lai Financial Department, BCG Glass Industry Inc., New York, NY, 10017, United States Author

DOI:

https://doi.org/10.71222/br77ty76

Keywords:

credit risk assessment, data-driven, decision tree, support vector machine, algorithm fairness

Abstract

With the rapid development of data-driven technology, the financial sector is increasingly reliant on data-driven approaches to credit risk assessment. This paper analyzes the application of decision tree, support vector machine, neural network and other models in credit risk assessment, discusses the current problems of data quality, bias, transparency and computing resources, and puts forward optimization strategies, such as strengthening data cleaning, reducing data bias, improving algorithm fairness, enhancing model transparency and optimizing computing resource allocation. The goal is to improve the accuracy and efficiency of assessments.

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