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This paper reviewed the literature describing use of data mining algorithms including statistical test, regression analysis, Neural Network, decision tree, Bayesian network etc for financial accounting fraud detection

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques.

International Journal of Computer Applications, no. 1 (2012): 37-47

Cited: 169|Views48
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Abstract

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use o...More

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Introduction
  • With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) have received considerable attention from the investors, academic researchers, media, the financial community and regulators.
  • The literature is missing a universally accepted definition of financial fraud, researcher has defined it as ―a deliberate act that is contrary to law, rule, or policy with intent to obtain unauthorized financial benefit‖ [4] and ―intentional misstatements or omission of amount by deceiving users of financial statement, especially investors and creditors‖ [5].
  • The accounting fraud is defined by accounting professionals as ―deliberate and improper manipulation of the recording of data in financial statements in order to achieve an operating profit of the company and appear better than it is‖ [6]
Highlights
  • With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) have received considerable attention from the investors, academic researchers, media, the financial community and regulators
  • This paper presents a comprehensive review of the research literature on the application of data mining techniques to detect financial accounting fraud
  • The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field
  • To determine the main algorithms used for financial accounting fraud detection, we present a Review of data mining techniques identified in literature applied to the detection of financial fraud
  • This paper reviewed the literature describing use of data mining algorithms including statistical test, regression analysis, Neural Network, decision tree, Bayesian network etc for financial accounting fraud detection
  • The expert fuzzy classification techniques enable one to perform approximate reasoning that can improve performance in three ways
  • The researchers have not made any comparison so far, related with detecting effect and accuracy of Neural Network compared to regression model
Results
  • The logistic regression based accounting fraud detecting models are common in literature since the model based on logistic regression can reach up to 95.1% of detecting accuracy with significant expectation effect [41].
  • The expert fuzzy classification techniques enable one to perform approximate reasoning that can improve performance in three ways
Conclusion
  • This paper reviewed the literature describing use of data mining algorithms including statistical test, regression analysis, Neural Network, decision tree, Bayesian network etc for financial accounting fraud detection.
  • The researchers have not made any comparison so far, related with detecting effect and accuracy of Neural Network compared to regression model.
  • After correct allocation and proper training, Neural Network may perform great classification comparing with regression model.
  • There are other issues related with Neural Network like no clear explanation on connecting weight, complex accuracy and statistical reliability checking procedure, and lack of explanation
Tables
  • Table1: Research on Neural Network for Financial Accounting Fraud Detection
  • Table2: Research on Regression Models for Financial Accounting Fraud Detection
  • Table3: Research on Fuzzy Logic for Financial Accounting Fraud Detection
  • Table4: Research on Expert System and Genetic Algorithm for Financial Accounting Fraud Detection
Download tables as Excel
Funding
  • The logistic regression based accounting fraud detecting models are common in literature since the model based on logistic regression can reach up to 95.1% of detecting accuracy with significant expectation effect [41]
  • The expert fuzzy classification techniques enable one to perform approximate reasoning that can improve performance in three ways
Reference
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