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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Financial Fraud Prediction Models: A Review Of Research Evidence

[Full Text]

 

AUTHOR(S)

V.K.Wadhwa, A.K.Saini, S.Sanjay Kumar

 

KEYWORDS

Empirical fraud prediction, Fraud Triangle/Diamond, M-Score, Z-Score, Machine Learning & Artificial Intelligence for fraud prediction.

 

ABSTRACT

Despite reports about significant advances in techniques for prediction of financial frauds research findings till now do not provide specific evidence or tools for predicting frauds that could be averted. Researchers have explored different methods with varied degree of success relying on financial data as well as non-financial factors for their purpose. This paper reviews reported models/evidence including adaptations/improvements in the models used during investigation. Fraud triangle theory specified by Cressey in 1953 is at the foundation applied in empirical predictive modelling postulated by a number of researchers. Prominent contributors are Beasley, M.S. (1996)., Dechow, et al. (1996).,Beneish M. D. (1997)., Nieschwietz et al. (2000), Skousen and Wright (2008). Convergence of fraud triangle theory to fraud diamond theory was suggested by Wolf and Hermanson in 2004. This paper additionally reviews specific computational models known as Z-Score (Altman,1968 ), M-Score (Beneish, 1999, 2012), and computer software based models from Green B.P. & Choi J.H.(1997) Zaki & Theodoulidis (2013) and Arta & Seyrek, (2009). There is a noticeable changing trend in research going towards numerous investigations now using computer supported machine learning and artificial intelligence tools for prediction of financial frauds. At the end an assessment is made about degree of success achieved in prediction of financial frauds till date. Empirical fraud prediction, Fraud Triangle/Diamond, M-Score, Z-Score, Machine Learning & Artificial Intelligence for fraud prediction.

 

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