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VOL. 11, ISSUE 3 (2025)
AI and ML in fraud detection: An empirical analysis of their impact on financial institutions' risk management practices
Authors
Mojisola Oladunni Jacob-Udeme, Blessing Sunday
Abstract
The financial industry struggles against
extensive fraud activity so institutions need enhanced detection systems to
deal with these risks effectively. The research observes how artificial
intelligence (AI) and machine learning (ML) systems affect fraud identification
processes in financial institutions while managing their risks. The
implementation of AI and ML technologies produces three primary benefits
because they boost fraud detection precision while minimizing false alerts and
optimizing operational performance. These technologies support compliance
frameworks and construct customer confidence but organizations should tackle
requirements such as data wellness along with system unification and moral
issues. This research directly addresses the question of AI and ML impact on
fraud detection by contradicting the assumption that the two elements work
independently. These findings demonstrate why proper strategic planning with
regulatory adjustment combined with inter-institutional cooperation is needed
to optimize the value of AI and ML in fraud prevention systems. Research should
concentrate on developing sustainable AI implementation strategies alongside
long-term regulatory measures which will boost fraud prevention abilities of
finance institutions.
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Pages:29-35
How to cite this article:
Mojisola Oladunni Jacob-Udeme, Blessing Sunday "AI and ML in fraud detection: An empirical analysis of their impact on financial institutions' risk management practices". International Journal of Commerce and Management Research, Vol 11, Issue 3, 2025, Pages 29-35
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