Enhancing Banking Fraud Detection and Risk Mitigation Using Advanced Machine Learning Techniques

Gautam Kumar Mishra; Gurjeet Singh; sudhir Pathak

doi:10.36948/ijfmr.2026.v08i01.65768

Enhancing Banking Fraud Detection and Risk Mitigation Using Advanced Machine Learning Techniques

Author(s)	Mr. Gautam Kumar Mishra, Prof. Dr. Gurjeet Singh, Prof. Dr. sudhir Pathak
Country	India
Abstract	The rapid digital transformation of financial services has changed the way transactions work, but it has also made people more vulnerable to new and quickly changing types of fraud. As the number of transactions rises and financial operations become more connected, fraud poses a serious threat to people, businesses, and the stability of financial systems as a whole. Traditional ways of finding fraud, which rely on set rules and fixed statistical assumptions, are becoming less and less effective against new and changing fraud strategies. Machine learning has become a strong alternative because it can look at enormous amounts of data, detect complicated behavioral patterns, and keep up with new types of fraud. This paper looks into how machine learning can help detect and stop fraud in financial transactions. It does this by examining its function, real-world applications, issues with use, and long-term financial security impact. This study shows how machine learning models can help with real-time transaction monitoring, finding unusual activity, and adjusting risk assessments by using real-life examples of credit card fraud, account takeover, and money laundering. This study closely examines the primary issues that arise when utilizing machine learning, including data quality, privacy protection, model transparency, system integration, and implementation costs. Machine learning has a lot of potential to improve the accuracy and efficiency of fraud detection, but to fully realize its benefits, these technical and organizational barriers must be overcome. The goal of this paper is to provide banks, technology professionals, and policymakers a complete picture of how to use machine learning to stop fraud and to suggest ways for future research and collaboration between the two fields.
Keywords	Keywords: fraud prevention; financial security; data privacy; machine learning; and fraud in financial transactions.
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-07
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.65768
Short DOI	https://doi.org/hbjmgh

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 6 Cover Page Vol 7 Isu 5 Cover Page Vol 7 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Enhancing Banking Fraud Detection and Risk Mitigation Using Advanced Machine Learning Techniques

Share this