Author(s)

Xin Jin ¹

Affiliation(s)

¹ Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai, China

Corresponding Author

Xin Jin

ABSTRACT

This paper discusses the application of machine learning technology in financial risk modeling and fraud detection, aiming at reducing commercial financial risks. Based on the Internet, big data and supercomputing technology, machine learning has significantly enhanced the ability of financial institutions to extract and interpret information through automatic data analysis, and thus greatly improved their risk control efficiency. The research covers the nonlinear association identification of supervised learning in credit risk assessment, the new fraud detection mode of unsupervised learning in fraud detection, and the efficient performance of deep learning in abnormal transaction identification. Empirical analysis shows that machine learning can significantly improve the accuracy of credit risk scoring model, accelerate the detection speed of fraudulent transactions, and achieve remarkable results in the verification of anti-money laundering in trade financing, effectively shortening the processing time and improving business efficiency. This study not only provides a theoretical basis for financial institutions to build an efficient internal risk management system, but also provides practical guidance for regulatory authorities to improve the external risk control environment, which is of great significance to improving the risk prevention and control ability of the financial system.

KEYWORDS

Machine Learning, Financial Risk Modeling, Fraud Detection, Credit Risk Assessment

CITE THIS PAPER

Xin Jin, Financial Risk Modeling and Fraud Detection Using Machine Learning: A Method to Reduce Business Risk. Financial Engineering and Risk Management (2025) Vol. 8: 185-192. DOI: http://dx.doi.org/10.23977/ferm.2025.080222.

REFERENCES

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[3] Zhou Yingxue. Cross-domain spillover of systemic financial risks and policy responses [D]. Jilin University, 2025.
[4] Xu Yi. Analysis of the application of machine learning in financial risk management [J]. Journal of anshun university, 2019,21(05):110-114.
[5] Long Huihui. Research and prototype realization of credit risk control early warning method based on machine learning [D]. University of Electronic Science and Technology of China, 2020.
[6] Li Zimu. Research on financial risk control model and algorithm based on machine learning [D]. north china university of technology, 2019.
[7] Li Jingzhao, Zhang Yuxin, Wang Aojun. Research on Internet Financial Risk Management [J]. Cooperative Economy and Technology, 2024,(13):52-54.
[8] Zhang Hua. Research on User Credit Evaluation Model Based on Telecommunication Data [D]. Beijing University of Posts and Telecommunications, 2018.

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