Financial Risk Control and Management Based on Machine Learning
Research Article
Open Access
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Financial Risk Control and Management Based on Machine Learning

Xinyu Liu 1*
1 Mathematics, Rutgers University, Suitbert Road, New Brunswick, United States
*Corresponding author: xl1043@scarletmail.rutgers.edu
Published on 28 October 2025
Journal Cover
ACE Vol.202
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-497-7
ISBN (Online): 978-1-80590-498-4
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Abstract

Effective financial risk management is critically important in the financial field, as it safeguards institutional stability and ensures sustainable economic growth. However, traditional methods, such as the linear regression model, have some limitations when addressing complex and modern risk prediction, which is challenging to apply in the big data age. With a focus on credit risk and operational risk, this study aims to address problems by applying machine learning techniques. For credit risk management, a precise model will be proposed, which integrates Extreme Gradient Boosting (XGBoost) for basic judgment and Long Short-Term Memory (LSTM) for analyzing suspected behavioral data. For operational risk, a two-layer detection model is introduced, employing Isolation Forest for rapid filtering and Prophet time series model for in-depth analysis. Final results indicate that proposed approaches have better performance than previous models in terms of accuracy and efficiency. This research presents a scalable and interpretable solution for risk management, although it also has some potential drawbacks, such as missing data.

Keywords:

machine learning, financial risk management, credit risk, operational risk

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Liu,X. (2025). Financial Risk Control and Management Based on Machine Learning. Applied and Computational Engineering,202,31-38.

References

[1]. Chakraborty, G. (2020). Evolving profiles of financial risk management in the era of digitization: The tomorrow that began in the past. Journal of Public Affairs, 20(2), e2034.

[2]. Horcher, K. A. (2011). Essentials of financial risk management. John Wiley & Sons. Retrieved from https: //books.google.com/books?hl=en& lr=& id=X__zoNzVh-QC& oi=fnd& pg=PT8& dq=Essentials+of+Financial+Risk+Management& ots=6uQ0cQkI-D& sig=LdAnZuV_1BUwonbsjt8Gv4hPmsE.

[3]. Hopkin, P. (2018). Fundamentals of risk management: understanding, evaluating and implementing effective risk management. Kogan Page Publishers.

[4]. Pang, S., Hou, X., & Xia, L. (2021). Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine. Technological Forecasting and Social Change, 165, 120462.

[5]. Aloqab, A., Alobaidi, F., & Raweh, B. (2018). Operational risk management in financial institutions: An overview. Business and economic research, 8(2), 11-32.

[6]. RISK, F. (2010). ELEMENTS OF FINANCIAL RISK MANAGEMENT. Retrieved from https: //www.academia.edu/34311333/Elements_of_Financial_Risk_Management.

[7]. Bello, O. A. (2023). Machine learning algorithms for credit risk assessment: an economic and financial analysis.  International Journal of Management,   10(1), 109-133. Retrieved from https: //eajournals.org/ijmt/wp-content/uploads/sites/69/2024/06/Machine-Learning-Algorithms.pdf.

[8]. Mashrur, A., Luo, W., Zaidi, N. A., & Robles-Kelly, A. (2020). Machine learning for financial risk management: a survey. Ieee Access, 8, 203203-203223

[9]. Addy, W. A., Ajayi-Nifise, A. O., Bello, B. G., Tula, S. T., Odeyemi, O., & Falaiye, T. (2024). Machine learning in financial markets: A critical review of algorithmic trading and risk management. International Journal of Science and Research Archive, 11(1), 1853-1862.

[10]. SULTAN, M. (2025). Machine Learning Models for Financial Risk Assessment. Retrieved from https: //www.researchgate.net/publication/390661682_Machine_Learning_Models_for_Financial_Risk_Assessment.

[11]. Guan, C., Suryanto, H., Mahidadia, A., Bain, M., & Compton, P. (2023). Responsible credit risk assessment with machine learning and knowledge acquisition. Human-Centric Intelligent Systems, 3(3), 232-243.

[12]. Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1), 111-138.

[13]. Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. Disrupting finance, 33-50.

[14]. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203-216.

[15]. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

[16]. Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Data analytics for operational risk management. Decis. Sci., 51(6), 1316-1319.

[17]. Eceiza, J., Kristensen, I., Krivin, D., Samandari, H., & White, O. (2020). The future of operational-risk management in financial services.  Preuzeto,   17, 2022. Retrieved from https: //www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/The%20future%20of%20operational%20risk%20management%20in%20financial%20services/The-future-of-operational-risk-management-in-financial-services-vF.pdf.

[18]. Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: an application to default risk analysis.

References

[1]. Chakraborty, G. (2020). Evolving profiles of financial risk management in the era of digitization: The tomorrow that began in the past. Journal of Public Affairs, 20(2), e2034.

[2]. Horcher, K. A. (2011). Essentials of financial risk management. John Wiley & Sons. Retrieved from https: //books.google.com/books?hl=en& lr=& id=X__zoNzVh-QC& oi=fnd& pg=PT8& dq=Essentials+of+Financial+Risk+Management& ots=6uQ0cQkI-D& sig=LdAnZuV_1BUwonbsjt8Gv4hPmsE.

[3]. Hopkin, P. (2018). Fundamentals of risk management: understanding, evaluating and implementing effective risk management. Kogan Page Publishers.

[4]. Pang, S., Hou, X., & Xia, L. (2021). Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine. Technological Forecasting and Social Change, 165, 120462.

[5]. Aloqab, A., Alobaidi, F., & Raweh, B. (2018). Operational risk management in financial institutions: An overview. Business and economic research, 8(2), 11-32.

[6]. RISK, F. (2010). ELEMENTS OF FINANCIAL RISK MANAGEMENT. Retrieved from https: //www.academia.edu/34311333/Elements_of_Financial_Risk_Management.

[7]. Bello, O. A. (2023). Machine learning algorithms for credit risk assessment: an economic and financial analysis.  International Journal of Management,   10(1), 109-133. Retrieved from https: //eajournals.org/ijmt/wp-content/uploads/sites/69/2024/06/Machine-Learning-Algorithms.pdf.

[8]. Mashrur, A., Luo, W., Zaidi, N. A., & Robles-Kelly, A. (2020). Machine learning for financial risk management: a survey. Ieee Access, 8, 203203-203223

[9]. Addy, W. A., Ajayi-Nifise, A. O., Bello, B. G., Tula, S. T., Odeyemi, O., & Falaiye, T. (2024). Machine learning in financial markets: A critical review of algorithmic trading and risk management. International Journal of Science and Research Archive, 11(1), 1853-1862.

[10]. SULTAN, M. (2025). Machine Learning Models for Financial Risk Assessment. Retrieved from https: //www.researchgate.net/publication/390661682_Machine_Learning_Models_for_Financial_Risk_Assessment.

[11]. Guan, C., Suryanto, H., Mahidadia, A., Bain, M., & Compton, P. (2023). Responsible credit risk assessment with machine learning and knowledge acquisition. Human-Centric Intelligent Systems, 3(3), 232-243.

[12]. Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1), 111-138.

[13]. Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. Disrupting finance, 33-50.

[14]. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203-216.

[15]. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

[16]. Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Data analytics for operational risk management. Decis. Sci., 51(6), 1316-1319.

[17]. Eceiza, J., Kristensen, I., Krivin, D., Samandari, H., & White, O. (2020). The future of operational-risk management in financial services.  Preuzeto,   17, 2022. Retrieved from https: //www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/The%20future%20of%20operational%20risk%20management%20in%20financial%20services/The-future-of-operational-risk-management-in-financial-services-vF.pdf.

[18]. Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: an application to default risk analysis.

Cite this article

Liu,X. (2025). Financial Risk Control and Management Based on Machine Learning. Applied and Computational Engineering,202,31-38.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-497-7(Print) / 978-1-80590-498-4(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.202
ISSN: 2755-2721(Print) / 2755-273X(Online)

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