A complete literature review on financial fraud detection applying data mining techniques
by Subhas Barman; Utpal Pal; Md. Asif Sarfaraj; Biswajit Biswas; Animesh Mahata; Palash Mandal
International Journal of Trust Management in Computing and Communications (IJTMCC), Vol. 3, No. 4, 2016

Abstract: Financial fraud is defined as unlawful or criminal duplicity attempted to result to organisational or personal gain. It is a big threat to the economics of a firm, corporate sector, government or ordinary customers in the form of credit card fraud, insurance fraud, and financial statement fraud. Several approaches exist in the literature of financial fraud detection. But, due to the inefficiency of those approaches, researchers leverage data mining techniques to detect financial fraud. This paper aims to build a systematic academic review of financial fraud detection approaches based on data mining techniques in the recent years. In the practice, different data mining techniques namely: K-nearest neighbour, decision tree, fuzzy logic, logistic model, Bayesian belief network, naïve Bayes, Beneish M-Score model, Benfords law, Altman Z-score have been applied to improve the accuracy of fraud detection. In this paper, existing financial fraud detection techniques are compared with the advantages and limitations of the techniques.

Online publication date: Wed, 14-Jun-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Trust Management in Computing and Communications (IJTMCC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com