A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is required only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.

[1]  K. Vanhoof,et al.  A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes, The IFR² Framework , 2009 .

[2]  Ashutosh Deshmukh,et al.  A rule-based fuzzy reasoning system for assessing the risk of management fraud , 1998, Intell. Syst. Account. Finance Manag..

[3]  D. Cressey,et al.  Why Managers Commit Fraud* , 1986 .

[4]  Kenneth O. Cogger,et al.  Neural network detection of management fraud using published financial data , 1998, Intell. Syst. Account. Finance Manag..

[5]  Randy S. Weinberg,et al.  Using Benford’s law and neural networks as a review procedure , 1998 .

[6]  Rajendra P. Srivastava,et al.  Detection of management fraud: a neural network approach , 1995, Proceedings the 11th Conference on Artificial Intelligence for Applications.

[7]  M. Beasley An Empirical Analysis of the Relation between Board of Director Composition and Financial Statement Fraud , 1998 .

[8]  Ashok N. Srivastava,et al.  Data Mining: Concepts, Models, Methods, and Algorithms , 2005, J. Comput. Inf. Sci. Eng..

[9]  Byron J. Pike,et al.  Auditors' responsibility for fraud detection: New wine in old bottles? , 2013 .

[10]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[11]  David C. Yen,et al.  An investigation of Zipf's Law for fraud detection (DSS#06-10-1826R(2)) , 2008, Decis. Support Syst..

[12]  Efraim Turban,et al.  Decision Support and Business Intelligence Systems (8th Edition) , 2006 .

[13]  C. Zopounidis,et al.  Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques , 2002 .

[14]  Charles Elkan,et al.  Magical thinking in data mining: lessons from CoIL challenge 2000 , 2001, KDD '01.

[15]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[16]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[17]  Shiguo Wang,et al.  A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[18]  Niall M. Adams,et al.  Off-the-peg and bespoke classifiers for fraud detection , 2008, Comput. Stat. Data Anal..

[19]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[20]  Praveen Pathak,et al.  Detecting Management Fraud in Public Companies , 2010, Manag. Sci..

[21]  Efraim Turban,et al.  Decision Support and Business Intelligence Systems (8th Edition) , 2006 .

[22]  Prabin Kumar Panigrahi,et al.  A Review of Financial Accounting Fraud Detection based on Data Mining Techniques , 2012, ArXiv.

[23]  Vadlamani Ravi,et al.  Detection of financial statement fraud and feature selection using data mining techniques , 2011, Decis. Support Syst..

[24]  B. Green,et al.  Assessing the risk of management fraud through neural network technology , 1997 .

[25]  Gaurav Kapoor,et al.  Detecting evolutionary financial statement fraud , 2011, Decis. Support Syst..

[26]  Xiaoguang Yang,et al.  False Financial Statements: Characteristics of China's Listed Companies and CART Detecting Approach , 2008, Int. J. Inf. Technol. Decis. Mak..

[27]  Ashutosh Deshmukh,et al.  A rule-based fuzzy reasoning system for assessing the risk of management fraud , 1998 .

[28]  T. Singleton,et al.  Fraud auditing and forensic accounting , 2010 .

[29]  Michael J. Cerullo,et al.  Using neural networks to predict financial reporting fraud: Part 1 , 1999 .

[30]  Z. Rezaee,et al.  Financial Statement Fraud: Prevention and Detection , 2002 .

[31]  Hian Chye Koh,et al.  Going concern prediction using data mining techniques , 2004 .

[32]  Johan L. Perols Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms , 2011 .

[33]  Eija Koskivaara Artificial Neural Networks in Auditing : State of the Art , 2003 .

[34]  Deniz Senturk-Doganaksoy,et al.  A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud , 2007, Intell. Syst. Account. Finance Manag..

[35]  Chen-Fu Chien,et al.  Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry , 2008, Expert Syst. Appl..