Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
University of Technology Malaysia · Sultan Zainal Abidin University · +3 more institutions
Abstract
Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review…
Citation impact
- FWCI
- 42.86
- Percentile
- 100%
- References
- 127
Authors
9- AAAbdulalem AliCorresponding
University of Technology Malaysia
- SAShukor Abd RazakCorresponding
University of Technology Malaysia, Sultan Zainal Abidin University
- SHSiti Hajar Othman
University of Technology Malaysia
- TATaiseer Abdalla Elfadil Eisa
King Khalid University
- AAArafat Al-DhaqmCorresponding
University of Technology Malaysia
Topics & keywords
- Credit card fraud
- Computer science
- Credit card
- Support vector machine
- Artificial intelligence
- Machine learning
- Systematic review
- World Wide Web
- Reduced inequalities