Review Article

Artificial Intelligence in Financial Risk Management: A Systematic Review of Applications, Performance, and Challenges

Abstract

The financial sector is witnessing a transformative age driven by AI’s predictive analytics, automated system monitoring, and intelligent decision-making solutions. AI is reshaping financial risk, providing predictive analytics, automatic system monitoring, and clever decision-making tools. This study is a systematic review which addresses the role of AI solutions in financial risk management, worries and results, and a recap of the previous research that took place between 2014 and 2025. The search was undertaken using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) and included papers found in the three main databases: IEEE Xplore, Scopus and Web of Science. Twenty-three studies met the inclusion criteria and were selected for the second phase of-eligible studies, final synthesis. The areas where machine and deep learning techniques were identified are fraud detection, credit risk prediction, anti-money laundering system, operational risk management, and cyber security and financial risk in supply chain – matching the findings. What was most frequently cited in the literature was a combination of several methods, like random forest, support vector machine and gradient boosting algorithms, to convolutional neural networks, long short-term memory networks, or even a combination of both. AI model-based systems have been shown to outperform traditional statistical/rules-based systems in terms of accuracy (+); anomaly detection and classification (-); etc., as per several studies. Equality and trustworthy AI systems for addressing transparency, fairness, interpretation and regulatory compliance at financial institutions, with a greater focus, were also noted in the review. However, there are still some factors that need to be taken into account: There is limited public data available, computational complexity, data imbalance and potential cyber-security issues, as well as the creation of potential algorithmic bias. Lastly, it’s worth mentioning the great opportunities AI offers in financial risk management, while de-emphasizing the importance of just, cyberresilient and AI framework validated AI governance models in order to establish a common set of standards for sustainability and applicability of AI technology in the dynamic financial landscape.

Author Affiliation:

Damilola Ayodele Ojo (ORCID)*: Project Management, College of Business, Missouri State University, Springfield, Missouri, USA.

Adebayo Fatai Lamidi (ORCID): Department of Banking and Finance, Osun state University, Osogbo, Nigeria.

Cyril Worlanyo Klu (ORCID): Industrial and Systems Engineering Department, North Carolina Agricultural and Technical State University, Greensboro, USA.

Jacob Miracle Godswill (ORCID): Rivers State University of Science and technology, Port Harcourt, Rivers State, Nigeria.

Charity Eshoyemele Oshozekhai (ORCID): Marketing Department, Auchi polytechnic, Auchi, Edo State, Nigeria.

How To Cite: D.A. Ojo, A.F. Lamidi , C.W. Klu, J.M. Godswill and C.E. Oshozekhai. Artificial Intelligence in Financial Risk Management: A Systematic Review of Applications, Performance, and Challenges. Journal of Computers and Applications, 2(1):24–35, 2026. https://doi.org/10.58613/jca213