Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review

被引:40
作者
Ali, Abdulalem [1 ]
Abd Razak, Shukor [1 ,2 ]
Othman, Siti Hajar [1 ]
Eisa, Taiseer Abdalla Elfadil [3 ]
Al-Dhaqm, Arafat [1 ]
Nasser, Maged [4 ]
Elhassan, Tusneem [1 ]
Elshafie, Hashim [5 ]
Saif, Abdu [6 ]
机构
[1] Univ Teknol Malaysia, Fac Engn, Sch Comp, Skudai 81310, Malaysia
[2] Univ Sultan Zainal Abidin, Fac Informat & Comp, Kuala Terengganu 21300, Malaysia
[3] King Khalid Univ, Dept Informat Syst Girls Sect, Mahayil 62529, Saudi Arabia
[4] Univ Sains Malaysia, Sch Comp Sci, Gelugor 11800, Malaysia
[5] King Khalid Univ, Coll Comp Sci, Abha 61421, Saudi Arabia
[6] Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, Malaysia
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 19期
关键词
financial fraud; fraud detection; machine learning; data mining; systematic literature review; Kitchenham approach; CREDIT CARD FRAUD; STATEMENT FRAUD; ALGORITHM; ACCOUNTS;
D O I
10.3390/app12199637
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
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页数:24
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