AI-Powered Predictive Analytics for Financial Risk Management in U.S. Markets
DOI:
https://doi.org/10.4108/airo.9532Keywords:
Financial risk management, AI-powered, predictive analytics, CatBoost, SVM, decision-making, U.S. marketsAbstract
In the fast-changing environment of financial complexity, efficient risk management is vital for economic stability as well as for growth. In this study, we present a robust AI-powered predictive analytics framework to improve financial risk classification in U.S. markets. The framework utilizes advanced machine learning techniques, a hybrid CatBoost and SVM model that allows it to solve challenges like class imbalance in a high-dimensional dataset while maintaining interpretable models. To probe errors, we use techniques such as Principal Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE) for data quality and fairness in classification. Comprehensive experiments on a financial risk dataset are conducted to evaluate the framework at which it achieves high accuracy (95.93%) and F1-score (0.95) when compared to traditional machine learning models such as Logistic Regression and Random Forest. Furthermore, a feature importance analysis identifies important predictors of financial risk such as Total Debt-to-Income Ratio, Loan Duration, and Interest Rate, providing actionable on decision-making. Additionally, the proposed approach is not only highly scalable but it is also interpretable and adaptable to the dynamic demands of financial institutions. This study serves as a benchmark for predicting analytics for dealing with risk-associated challenges, leading to informed decision-making to ensure economic stability by integrating AI and machine learning in financial systems.
Downloads
References
[1] J. Xiao, J. Wang, W. Bao, S. Bi, and T. Deng, "Research on the application of data analysis in predicting financial risk," Financial Engineering and Risk Management, vol. 7, no. 4, pp. 183–188, 2024.
[2] M. S. Murugan and S. K. T, "Large-scale data-driven financial risk management & analysis using machine learning strategies," Measurement: Sensors, vol. 27, p. 100756, 2023/06/01/ 2023, doi: https://doi.org/10.1016/j.measen.2023.100756.
[3] K. Valaskova, T. Kliestik, L. Svabova, and P. Adamko, "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, vol. 10, no. 7, doi: 10.3390/su10072144.
[4] J. Grable and R. H. Lytton, "Financial risk tolerance revisited: the development of a risk assessment instrument☆," Financial Services Review, vol. 8, no. 3, pp. 163–181, 1999/01/01/ 1999, doi: https://doi.org/10.1016/S1057-0810(99)00041-4.
[5] S. Ahmed, M. M. Alshater, A. E. Ammari, and H. Hammami, "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, vol. 61, p. 101646, 2022/10/01/ 2022, doi: https://doi.org/10.1016/j.ribaf.2022.101646.
[6] M. C. Dyball and R. Seethamraju, "Client use of blockchain technology: exploring its (potential) impact on financial statement audits of Australian accounting firms," Accounting, Auditing & Accountability Journal, vol. 35, no. 7, pp. 1656–1684, 2022, doi: 10.1108/AAAJ-07-2020-4681.
[7] T. P. Nugrahanti, "Analyzing the evolution of auditing and financial insurance: tracking developments, identifying research frontiers, and charting the future of accountability and risk management," West Science Accounting and Finance, vol. 1, no. 02, pp. 59–68, 2023.
[8] S. Pattyam, "‘Ai-driven financial market analysis: Advanced techniques for stock price prediction, risk management, and automated trading," African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, pp. 100–135, 2021.
[9] H. A. Javaid, "Ai-driven predictive analytics in finance: Transforming risk assessment and decision-making," Advances in Computer Sciences, vol. 7, no. 1, 2024.
[10] M. Schmitt, "Automated machine learning: AI-driven decision making in business analytics," Intelligent Systems with Applications, vol. 18, p. 200188, 2023/05/01/ 2023, doi: https://doi.org/10.1016/j.iswa.2023.200188.
[11] B. Biswas, "IT in Improving Integrity and Productivity in Supply Chain Management.," Transactions on Banking, Finance, and Leadership Informatics, vol. 1, no. 1, pp. 1–7, 2024, doi: https://doi.org/10.63471/tbfli24001.
[12] M. J. H. Mani Prabha, Jarin Tias Meraj, "AI-Driven Financial Security: Innovations in Protecting Assets and Mitigating Risks.," Advances in Machine Learning, IoT and Data Security, vol. 1, no. 1, pp. 14–23, 2024, doi: https://doi.org/10.63471/amlids24004.
[13] M. A. A. M. Md Saddam Hosain, Dr. Joseph P. Siegmund, "Systemic Risk and Financial Stability: Measurement and Policy Implications.," Transactions on Banking, Finance, and Leadership Informatics, vol. 1, no. 1, pp. 8–11, 2024, doi: https://doi.org/10.63471/tbfli24002.
[14] X. Zhong and D. Enke, "Forecasting daily stock market return using dimensionality reduction," Expert Systems with Applications, vol. 67, pp. 126–139, 2017/01/01/ 2017, doi: https://doi.org/10.1016/j.eswa.2016.09.027.
[15] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," J. Artif. Int. Res., vol. 16, no. 1, pp. 321–357, 2002.
[16] H. Dong, R. Liu, and A. W. Tham, "Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation," Journal of Risk and Financial Management, vol. 17, no. 2, doi: 10.3390/jrfm17020050.
[17] A. H. Md. Mehedi Hasan, "Cybersecurity Strategies for Businesses: Protecting Data in a Digital World.," Advances in Machine Learning, IoT and Data Security, vol. 1, no. 1, pp. 24–29, 2024, doi: https://doi.org/10.63471/amlids24005.
[18] G. Valdrighi et al., "Best Practices for Responsible Machine Learning in Credit Scoring," arXiv preprint arXiv:2409.20536, 2024.
[19] E. I. Altman, "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy," The Journal of Finance, vol. 23, no. 4, pp. 589–609, 1968, doi: 10.2307/2978933.
[20] M. K. K. S. Syed Nazmul Hasan, Oli Ahammed Sarker, Md Redwan Hussain, Jarin Tias Meraj, "Blockchain Based Security Solutions for Banking Information Technology," Transactions on Banking, Finance, and Leadership Informatics, vol. 1, no. 1, pp. 25–29, 2024, doi: https://doi.org/10.63471/tbfli24005.
[21] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001/10/01 2001, doi: 10.1023/A:1010933404324.
[22] T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, 2016. [Online]. Available: https://doi.org/10.1145/2939672.2939785.
[23] P.-F. Pai and W.-C. Hong, "Support vector machines with simulated annealing algorithms in electricity load forecasting," Energy Conversion and Management, vol. 46, no. 17, pp. 2669–2688, 2005/10/01/ 2005, doi: https://doi.org/10.1016/j.enconman.2005.02.004.
[24] M.-F. Hsu and P.-F. Pai, "Incorporating support vector machines with multiple criteria decision making for financial crisis analysis," Quality & Quantity, vol. 47, no. 6, pp. 3481–3492, 2013/10/01 2013, doi: 10.1007/s11135-012-9735-y.
[25] J. B. Heaton, N. G. Polson, and J. H. Witte, "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, vol. 33, no. 1, pp. 3–12, 2017/01/01 2017, doi: https://doi.org/10.1002/asmb.2209.
[26] B. Liao, Z. Huang, X. Cao, and J. Li, "Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach," Mathematics, vol. 10, no. 13, doi: 10.3390/math10132160.
[27] L. J. Mena et al., "Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off," Humanities and Social Sciences Communications, vol. 11, no. 1, p. 1540, 2024/11/14 2024, doi: 10.1057/s41599-024-04047-5.
[28] V. Amarnadh and N. R. Moparthi, "Range control-based class imbalance and optimized granular elastic net regression feature selection for credit risk assessment," Knowledge and Information Systems, vol. 66, no. 9, pp. 5281–5310, 2024/09/01 2024, doi: 10.1007/s10115-024-02103-9.
[29] M. R. M. Md. Mehedi Hasan, " Cloud Computing in Banking Flexibility and Scalability for Financial Institute.," Transactions on Banking, Finance, and Leadership Informatics, vol. 1, no. 1, pp. 17–24, 2024, doi: https://doi.org/10.63471/tbfli24004.
[30] G. Ke et al., "LightGBM: a highly efficient gradient boosting decision tree," presented at the Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 2017.
[31] Y. Wang, Z. Wu, J. Gao, C. Liu, and F. Guo, "A multi-level classification based ensemble and feature extractor for credit risk assessment," (in eng), PeerJ Comput Sci, vol. 10, p. e1915, 2024, doi: 10.7717/peerj-cs.1915.
[32] H. Lu and X. Hu, "Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach," Journal of the Knowledge Economy, vol. 15, no. 2, pp. 9824–9840, 2024/06/01 2024, doi: 10.1007/s13132-023-01601-5.
[33] T. Berhane, T. Melese, A. Walelign, and A. Mohammed, "A Hybrid Convolutional Neural Network and Support Vector Machine-Based Credit Card Fraud Detection Model," Mathematical Problems in Engineering, vol. 2023, no. 1, p. 8134627, 2023/01/01 2023, doi: https://doi.org/10.1155/2023/8134627.
[34] M. S. I. C. Rabeya Khatoon, "Predictive Analytics in Customer Relationship Management in the USA.," Journal of Business Venturing, AI and Data Analytics, vol. 1, no. 1, pp. 37–43, 2024, doi: https://doi.org/10.63471/jbvada24005.
[35] Y. N. Prajapati and M. Kumar, "A review paper on cause of heart disease using machine learning algorithms," Journal of Pharmaceutical Negative Results, pp. 9250–9259, 2022.
[36] U. Altunöz, "Prediction of Banking Credit Risk Using Logistic Regression and The Artificial Neural Network Models: A Case Study of English Banks," Sosyal Araştırmalar ve Davranış Bilimleri, vol. 10, no. 21, pp. 862–887, 2024.
[37] K.-H. Shih, C.-C. Cheng, and Y.-H. Wang, "Financial information fraud risk warning for manufacturing industry-using logistic regression and neural network," Romanian Journal of Economic Forecasting, vol. 14, no. 1, pp. 54–71, 2011.
[38] Z. Zheng, "Financial Risk Early Warning Model Combining SMOTE and Random Forest for Internet Finance Companies," J. Cases Inf. Technol., vol. 26, no. 1, pp. 1–21, 2024, doi: 10.4018/jcit.356504.
[39] B. Tan, Z. Gan, and Y. Wu, "The measurement and early warning of daily financial stability index based on XGBoost and SHAP: Evidence from China," Expert Systems with Applications, vol. 227, p. 120375, 2023/10/01/ 2023, doi: https://doi.org/10.1016/j.eswa.2023.120375.
[40] Z. Zhuo, "Research on Credit Card Overdue Risk Prediction based on CatBoost Model,” " presented at the in Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, 2024.
[41] M. Abdar et al., "A new nested ensemble technique for automated diagnosis of breast cancer," Pattern Recognition Letters, vol. 132, pp. 123–131, 2020/04/01/ 2020, doi: https://doi.org/10.1016/j.patrec.2018.11.004.
[42] M. A. Muslim et al., "New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning," Intelligent Systems with Applications, vol. 18, p. 200204, 2023/05/01/ 2023, doi: https://doi.org/10.1016/j.iswa.2023.200204.
[43] Á. González-Prieto, A. Brú, J. C. Nuño, and J. L. González-Álvarez, "Hybrid machine learning methods for risk assessment in gender-based crime," Knowledge-Based Systems, vol. 260, p. 110130, 2023/01/25/ 2023, doi: https://doi.org/10.1016/j.knosys.2022.110130.
[44] K. McDonnell, F. Murphy, B. Sheehan, L. Masello, and G. Castignani, "Deep learning in insurance: Accuracy and model interpretability using TabNet," Expert Systems with Applications, vol. 217, p. 119543, 2023/05/01/ 2023, doi: https://doi.org/10.1016/j.eswa.2023.119543.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Md Zikar Hossan, Muslima Begom Riipa, Md Azhad Hossain, Sweety Rani Dhar, Al Modabbir Zaman, Mohammad Hossain, Arif Hossen, Hasan Mahmud Sozib

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.