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Credit Evaluation System Based on FICO
- 1 School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, Henan Province, China
- 2 School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China
* Author to whom correspondence should be addressed.
Abstract
As credit transactions become more prevalent, financial institutions require effective methods to assess credit risk and reduce the likelihood of borrower default. In the U.S., the Fair Isaac Credit Organization (FICO) score is widely used by banks and insurers to evaluate personal creditworthiness. This paper aims to develop an automated credit scoring tool based on the FICO system to help financial institutions improve risk assessment. The paper leverages the random forest algorithm for data preprocessing and feature engineering to extract key variables, such as the borrower's financial status and credit history. To ensure data stability and interpretability, Information Value and Weight of Evidence techniques are applied to process these variables. Additionally, the Sigmoid function is used to map the model output to a range between 0 and 1, making it suitable for generating credit scores. This random forest algorithm helps handle non-linear relationships and missing data, while cross-validation enhances the model’s generalization ability. After training, the paper achieved an automated credit scoring system, closely aligned with the FICO scoring system. The model’s Area Under the Curve (AUC) value reached 0.84, indicating strong predictive accuracy and reliability. This tool enables financial institutions to more accurately assess credit risk, offering a robust, data-driven approach to improve decision-making and risk management.
Keywords
Credit Evaluation, FICO, Machine Learning, Python.
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Cite this article
Li,Y.;Shi,Y. (2024). Credit Evaluation System Based on FICO. Applied and Computational Engineering,96,48-55.
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 2nd International Conference on Machine Learning and Automation
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