Artificial Intelligence for Smarter Financial Decisions: A Comprehensive Analysis of Risk Assessment and Predictive Tools
Renuka Deshmukh
Department of School of Business, Dr Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India and Centre for Management and Marketing Innovation, COE for Business Innovation and Communication, Faculty of Management, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
Centre for Management and Marketing Innovation, COE for Business Innovation and Communication, Faculty of Management, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
Centre for Smart Systems and Automation, COE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
The advent of Artificial Intelligence (AI) has revolutionized the financial industry by enabling more accurate, efficient, and dynamic decision-making processes. This paper explores the transformative role of AI in financial risk assessment and the development of predictive tools that facilitate smarter financial decisions. It investigates how machine learning algorithms, natural language processing, and neural networks are deployed to assess credit risk, forecast market trends, detect fraud, and enhance portfolio management. By synthesizing recent advancements and real-world applications, this study evaluates the efficacy, reliability, and ethical considerations of AI-driven tools in finance. The paper also addresses the challenges of data quality, algorithmic bias, and regulatory compliance. Through a comprehensive analysis, it provides insights into the current landscape and future prospects of AI in shaping a resilient and intelligent financial ecosystem.
Arner, D. W., Barberis, J., & Buckley, R. P. (2023). Fintech, Regtech, and the Reconceptualization of Financial Regulation. Journal of Financial Regulation and Compliance, 31(1), 7-22.
T. Berg, V. Burg, A. Gombović, and M. Puri, “On the Rise of FinTechs – Credit Scoring using Digital Footprints,” National Bureau of Economic Research, Apr. 2018. doi: 10.3386/w24551.
Chen, C., Zhang, C., & Zhu, Y. (2023). Deep Learning in Credit Risk Modeling: A Survey. Expert Systems with Applications, 213, 118978.
Das, S. R., & Jagtiani, J. (2023). Machine Learning in Finance: The Case of Predicting Credit Card Delinquencies. Journal of Banking & Finance, 145, 106661.
Duarte, F., & Eisenbach, T. M. (2022). Fire-Sale Spillovers and Systemic Risk. Journal of Financial Economics, 146(3), 814-838.
Goodell, J. W., & Huynh, T. L. D. (2023). Financial Technology and Innovation: A Review of the FinTech Literature. Journal of Economic Surveys, 37(1), 134-162.
Huang, Y., Wang, Y., & Wang, Y. (2023). Risk Management in the Age of AI: Applications and Challenges. Risk Analysis, 43(2), 276-289.
E. Khandani, A. J. Kim, and A. W. Lo, “Consumer credit-risk models via machine-learning algorithms,” Journal of Banking & Finance, vol. 34, no. 11, pp. 2767–2787, Nov. 2010, doi: 10.1016/j.jbankfin.2010.06.001.
Li, B., & Li, Y. (2023). AI-Based Portfolio Optimization: A Comparative Study. Quantitative Finance, 23(4), 601-618.
Lin, W., & Wu, C. (2023). Predictive Modeling in Financial Markets: Integrating Big Data and AI. International Review of Financial Analysis, 86, 102575.
Luo, X., & Xia, Y. (2023). Natural Language Processing for Financial Text Analysis: Recent Advances and Applications. Journal of Computational Finance, 27(1), 33-57.
N. Sharma, P. William, K. Kulshreshtha, G. Sharma, Dr. B. Haralayya, and Y. Chauhan, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility,” SSRN Electronic Journal, 2024, doi: 10.2139/ssrn.5000303.
T. T, A. Haldorai, S. G, and A. Sasi, “Hybrid Machine Learning Methodology for Real Time Quality of Service Prediction and Ideal Spectrum Selection in CRNs,” Journal of Machine and Computing, pp. 1265–1276, Apr. 2025, doi: 10.53759/7669/jmc202505099.
Shrivastava, S. J. Suji Prasad, A. R. Yeruva, P. Mani, P. Nagpal, and A. Chaturvedi, “IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area,” Cybernetics and Systems, vol. 56, no. 1, pp. 21–32, Jan. 2023, doi: 10.1080/01969722.2023.2166243.
Zhang, W., & Zhou, L. (2023). Enhancing Financial Decision-Making with Explainable AI. Journal of Financial Data Science, 5(1), 45-61.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Renuka Deshmukh, Siow-Hooi Tan, Yi-Fei Tan and Anurag Shrivastava;
Writing- Original Draft Preparation: Renuka Deshmukh, Siow-Hooi Tan, Yi-Fei Tan and Anurag Shrivastava;
Visualization: Yi-Fei Tan and Anurag Shrivastava;
Investigation: Renuka Deshmukh and Siow-Hooi Tan;
Supervision: Yi-Fei Tan and Anurag Shrivastava;
Validation: Renuka Deshmukh and Siow-Hooi Tan;
Writing- Reviewing and Editing: Renuka Deshmukh, Siow-Hooi Tan, Yi-Fei Tan and Anurag Shrivastava;All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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Renuka Deshmukh
Department of School of Business, Dr Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India and Centre for Management and Marketing Innovation, COE for Business Innovation and Communication, Faculty of Management, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
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Cite this article
Renuka Deshmukh, Siow-Hooi Tan, Yi-Fei Tan and Anurag Shrivastava, “Artificial Intelligence for Smarter Financial Decisions: A Comprehensive Analysis of Risk Assessment and Predictive Tools”, Journal of Machine and Computing, vol.5, no.3, pp. 1642-1653, July 2025, doi: 10.53759/7669/jmc202505130.
