Machine Learning Applications in Financial Forecasting and Risk Management: Challenges and Practical Implications
DOI:
https://doi.org/10.54097/xm84hj47Keywords:
Machine learning, Financial forecast, Risk management, Financial Intelligence, Big Data FinanceAbstract
The digitalization of finance continues to advance, and the business and service models of the industry are constantly innovating. The traditional statistical analysis methods have been unable to meet the development needs of the current financial market. The financial market has a large volume of data, diverse types, and strong dynamics. Traditional statistical models, relying on linear assumptions, cannot capture the nonlinear correlations of the data, have limited analysis accuracy and application scope, and are difficult to meet the needs of modern financial precise analysis and refined risk control. This paper takes machine learning technology as the core of research and conducts application research around three core financial scenarios: quantitative market analysis, customer credit assessment, and financial risk prevention and control. Machine learning breaks through the linear limitations of traditional methods and can deeply explore the hidden features and potential patterns of massive financial data, effectively improving the effect of data analysis and assisting financial institutions in judging market trends and avoiding business risks. The implementation and application of machine learning still have many shortcomings. The domestic financial industry has not yet formed a unified data standard, and the data statistics standards of each institution are different, with insufficient data standardization and sharing. At the same time, the existing risk control models have poor interpretability and limited algorithm scene adaptability, and are difficult to adapt to complex and variable financial businesses. In combination with the strict regulatory requirements of the financial industry, this paper proposes improvement methods from three aspects: unified data standards, optimizing model performance, and upgrading scenario-based algorithms, to build a human-machine collaborative intelligent risk control system. This research result can provide practical basis for financial institutions to build intelligent risk control systems and improve risk management systems, and help the financial industry achieve intelligent and high-quality development.
Downloads
References
[1] Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273.
[2] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.
[3] Kim, A., Yang, Y., Lessmann, S., Ma, T., Sung, M. C., & Johnson, J. E. V. (2020). Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. European Journal of Operational Research, 283(1), 217–234.
[4] Duan, J. (2019). Financial system modeling using deep neural networks for effective risk assessment and prediction. Journal of the Franklin Institute, 356(8), 4716–4731.
[5] Liao, T., & Chen, X. (2020). Predictive models for financial forecasting: Integration of big data and AI technologies. Quantitative Finance, 20(6), 947–963.
[6] Liu, Y., & Zhang, J. (2020). Financial risk management in the era of big data and artificial intelligence. Financial Innovation, 6(3), 1–15.
[7] Khojasteh, M., & Daliri, M. R. (2021). Portfolio risk prediction using extreme gradient boosting. Computational Economics, 58(4), 1211–1234.
[8] Niu, B., Ren, J., & Li, X. (2019). Credit scoring using machine learning by combining social network information: Evidence from peer to peer lending. Information, 10(12), 377.
[9] Holzmann, H., & Rathke, A. (2021). Machine learning for value at risk forecasting: A review and comparison. Journal of Forecasting, 40(8), 1302–1326.
[10] Baruník, J., & Křehlík, T. (2021). Machine learning for realized covariance forecasting in finance. Computational Economics, 58(3), 987–1012.
[11] Bouteska, A., Abedin, M. Z., Hajek, P., & Yuan, K. (2024). Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods. International Review of Financial Analysis, 92, 103055.
[12] Del Nero, L., & Giudici, P. (2025). Machine learning models to predict stock market spillovers. Finance Research Letters, 86, 108508.
[13] Rao, C., Liu, M., Goh, M., & Wen, J. (2020). 2 stage modified random forest model for credit risk assessment of P2P network lending. Applied Soft Computing, 95, 106570.
[14] Zhu, L., Qiu, D., Ergu, D., Ying, C., & Liu, K. (2019). A study on credit risk assessment model based on machine learning. Computational Economics, 54(2), 715–735.
[15] Sirignano, J., & Cont, R. (2019). Machine learning for credit risk management. Journal of Financial Econometrics, 17(4), 692–731.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Mathematical Finance and Risk Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
