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Liczba wyników
2016 | 2 (16) | nr 4 | 45-64
Tytuł artykułu

Accounting Frauds - Review of Advanced Technologies to Detect and Prevent Frauds

Autorzy
Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In past decades, accounting fraud has adversely affected economies worldwide. Therefore, effective measures and methods ought to be employed in order to efficiently prevent and detect accounting fraud in a rapidly changing and technologybased business environment. Data mining methods can assist in prevention and detection of fraudulent transactions as it enables the use of past cases of fraud to build models that can recognize and spot the risk of fraud and can design new techniques for preventing fraudulent financial reporting. This article reviews the concept of accounting fraud, and focuses on some of the available data mining tools and methodologies, as well as other commuter-based techniques and tools that are available to order to assist in preventing accounting fraud and detecting if fraudulent acts have been committed. The article asserts the importance of using the available computer-based and data mining techniques as a prevention mechanism by detecting financial statement fraud, concluding that data mining software propose a good supporting procedure which offers an effective solution to the problem of detecting fraudulent transactions and accounting frauds. (original abstract)
Rocznik
Tom
Numer
Strony
45-64
Opis fizyczny
Twórcy
  • Jerusalem College of Technology, Israel
Bibliografia
  • Albrecht, C.C., Albrecht, W.S., Dunn, J.G., 2006, Can Auditors Detect Fraud: A Review of the Research Evidence. 2001, Journal of Forensic Accounting, vol. 2: 1-12.
  • Almeida, M.P.S.-B., 2009, Classification for Fraud Detection with Social Network Analysis, Dissertation, Engenharia Informatica e de Computadores.
  • Amer, M., Goldstein, M., 2012, Nearest-neighbor and Clustering Based Anomaly Detection Algorithms for Rapidminer, Proc. of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012): 1-12.
  • Ball, R., 2009, Market and Political/Regulatory Perspectives on the Recent Accounting Scandals, Journal of Accounting Research, 47(2): 277-323.
  • Chan, Y.D., Vasarhelyi, M.A., 2011, Innovation and Practice of Continuous Auditing, International Journal of Accounting Information Systems, 12 (2011): 152-160.
  • Flegel, U., Vayssiere, J., Bitz, G., 2010, A State of the Art Survey of Fraud Detection Technology, Insider Threats in Cyber Security, Springer US: 73-84.
  • Hake, E.R., 2005, Financial Illusion: Accounting for Profits in an Enron World, Journal of Economic Issues, vol. 39(3): 595-611.
  • Hannon, N., 2002, Accounting Scandals: Can XBRL Help?, Strategic Finance, 84. 2: 61-62.
  • Islam, I.A., Corney, M.W., Mohay, G.M., Clark, A.J., Bracher, S., Tobias, R., Flegel, U., 2010, Fraud Detection in ERP Systems Using Scenario Matching, Security and Privacy: Silver Linings in the Clouds. Brisbane Convention and Exhibition Center, Australia.
  • Jones, M.J., 2011, Creative Accounting, Fraud and International Accounting Scandals, John Wiley & Sons.
  • Kabir Usman, A., 2013, Critical Success Factors for Preventing E-banking Fraud, Journal of Internet Banking and Commerce, 18 (2): 1-16.
  • Keila, P.S., Skillicorn, D.B., 2005, Detecting Unusual and Deceptive Communication in Email, Centers for Advanced Studies Conference: 17-20.
  • Kennedy, K.A., 2012, An Analysis of Fraud: Causes, Prevention, and Notable Cases, http://scholars.unh.edu/cgi/viewcontent.cgi?article=1099&context=honors.
  • Khan, R.Q., Corney, M.W., Clark, A.J., Mohay, G.M., 2010, Transaction Mining for Fraud Detection in ERP Systems, Industrial Engineering and Management Systems, 9(2), in Press.
  • Kirkos, E., Manolopoulos, Y., Spathis, C., 2007, Data Mining Techniques for the Detection of Fraudulent Financial Statements, Expert Systems with Applications, vol. 32: 995-1003.
  • Kotsiantis, S., Koumanakos, E., Tampakas, V., Tzelepis, D., 2006, Forecasting Fraudulent Financial Statements using Data Mining, International Journal of Computational Intelligence, vol. 3, no. 2.
  • Kunz, M., Wilson, P., 2004, Computer Crime and Computer Fraud, College Park: University of Maryland, Department of Criminology and Criminal Justice.
  • Lendez, A.M., Korevec, J., 1999, How to Prevent and Detect Financial Statement Fraud, The Journal of Corporate Accounting and Finance, 11(1): 47-54.
  • Lou, Y.I., Wang, M.L., 2011, Fraud Risk Factor of the Fraud Triangle Assessing the Likelihood of Fraudulent Financial Reporting, Journal of Business & Economics Research (JBER), 7(2).
  • Panigrahi, P., Sharma, A., 2012, A Review of Financial Accounting Fraud Detection based on Data Mining Techniques, International Journal of Computer Applications (0975-8887), vol. 39, no. 1.
  • Pustylnick, I., 2009, Financial Data Set Used is Computerized Fraud Detection, Swiss Management Center, Transknowlogy Campus.
  • Roohani, S., Furusho, Y., Koizumi, M., 2009, XBRL: Improving Transparency and Monitoring Functions of Corporate Governance, International Journal of Disclosure and Governance, 6, November: 355-369.
  • Sadka, G., 2006, The Economic Consequences of Accounting Fraud in Product Markets: Theory and a Case from the US Telecommunications Industry (WorldCom), American Law and Economics Review, 8(3), 439-475.
  • Sheridan, B., Drew, J., 2012, The Future Is Now: XBRL Emerges as a Career Niche, Journal of Accountancy (June): 123-127.
  • Smith, A.D., Lias, A.R., 2007, Identity Theft and E-Fraud Driving CRM Information Exchanges, Hershey, PA: IGI Publishing.
  • The Real Time Economy, 2002, Economist.
  • Vadoodparast, M., Hamdan, A.R., 2015, Fraudulent Electronic Transaction Detection Using Dynamic KDA Model, International Journal of Computer Science and Information Security, 13(3): 90.
  • Yallapragada, R.R., Roe, C.W., Toma, A.G., 2012, Accounting Fraud, and White-collar Crimes in the United States, Journal of Business Case Studies, 8(2): 187-192.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171448108
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