Ali, Abdulalem and Abd Razak, Shukor and Othman, Siti Hajar and Eisa, Taiseer Abdalla Elfadil and Al-Dhaqm, Arafat and Nasser, Maged and Elhassan, Tusneem and Elshafie, Hashim and Saif, Abdu (2022) Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences-Basel, 12 (19). ISSN 2076-3417, DOI https://doi.org/10.3390/app12199637.
Full text not available from this repository.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 employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes popular ML techniques used for fraud detection, the most popular fraud type, and evaluation metrics. The reviewed articles showed that support vector machine (SVM) and artificial neural network (ANN) are popular ML algorithms used for fraud detection, and credit card fraud is the most popular fraud type addressed using ML techniques. The paper finally presents main issues, gaps, and limitations in financial fraud detection areas and suggests possible areas for future research.
| Item Type: | Article |
|---|---|
| Funders: | Deanship of Scientific Research at King Khalid University (RGP.2/49/43) |
| Uncontrolled Keywords: | Financial fraud; Fraud detection; Machine learning; Data mining; Systematic literature review; Kitchenham approach |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Aug 2023 08:06 |
| Last Modified: | 03 Aug 2023 08:06 |
| URI: | http://eprints.um.edu.my/id/eprint/41051 |
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