European Journal of Business Science and Technology 2021, 7(1):47-58 | DOI: 10.11118/ejobsat.2021.009

The Mediating Role of Big Data to Influence Practitioners to Use Forensic Accounting for Fraud Detection

Prabhat Mittal1, Amrita Kaur1, Pankaj Kumar Gupta2
1 University of Delhi, New Delhi, India
2 Jamia Milia Islamia, New Delhi, India

Globally, the financial industry in the recent times has witnessed various forms of fraudulent activities in the financial markets creating dilemma for the professionals, and the auditors who owns responsibility of ensuring accuracy and transparency. This article aims at finding the emergence of Big Data technology to fraud and forensic accounting by practitioner accountants in India. A research model and hypotheses has been developed to examine the relationship between the awareness level of forensic accounting, Big Data and intentions to use it for fraud detection using structural equation modeling. Results indicate that awareness of forensic accounting has a positive influence on practitioners’ intentions to its use for fraud detection. Big data technologies mediate the relationship between awareness and intentions to use for fraud detection. The results of the study are useful in implementation of Big Data technologies into the forensic accounting domain that can facilitate combating fraud.

Keywords: big data, fraud, forensic accounting, structural equation modeling
JEL classification: C1, C3, M4, O3

Received: March 28, 2021; Revised: July 11, 2021; Accepted: July 24, 2021; Published: August 9, 2021  Show citation

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Mittal, P., Kaur, A., & Kumar Gupta, P. (2021). The Mediating Role of Big Data to Influence Practitioners to Use Forensic Accounting for Fraud Detection. European Journal of Business Science and Technology, 7(1), 47-58. doi: 10.11118/ejobsat.2021.009
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    References

    1. Abdallah, A., Maarof, M. A. & Zainal, A. 2016. Fraud Detection System: A Survey. Journal of Network and Computer Applications, 68, 90-113. DOI: 10.1016/j.jnca.2016.04.007. Go to original source...
    2. ACFE (Association of Certified Fraud Examiners). 2014. Report To the Nations on Occupational Fraud and Abuse [online]. Available at: https://www.acfe.com/rttn-summary.aspx.
    3. Akoglu, L., Chandy, R. & Faloutsos, C. 2013. Opinion Fraud Detection in Online Reviews by Network Effects. In Kiciman, E., Ellison, N. B., Hogan, B., Resnick, P. & Soboroff, I. (eds.). Proceedings of the 7th International AAAI Conference on Weblogs and Social Media. ISBN 978-1-57735-610-3.
    4. Alles, M. G. 2015. Drivers of the Use and Facilitators and Obstacles of the Evolution of Big Data by the Audit Profession. Accounting Horizons, 29 (2), 439-449. DOI: 10.2308/acch-51067. Go to original source...
    5. Appelbaum, D., Kogan, A. & Vasarhelyi, M. A. 2017. Big Data and Analytics in the Modern Audit Engagement: Research Needs. Auditing: A Journal of Practice & Theory, 36 (4), 1-27. DOI: 10.2308/ajpt-51684. Go to original source...
    6. Bănărescu, A. 2015. Detecting and Preventing Fraud with Data Analytics. Procedia Economics and Finance, 32, 1827-1836. DOI: 10.1016/s2212-5671(15)01485-9. Go to original source...
    7. Bhatia, A. & Mittal, P. 2019. Big Data Driven Healthcare Supply Chain: Understanding Potentials and Capabilities. In Proceedings of the International Conference on Advancements in Computing & Management (ICACM-2019), 879-887. DOI: 10.2139/ssrn.3464217. Go to original source...
    8. Chin, W. W. & Newsted, P. R. 1999. Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares. In Hoyle, R. H. (ed.). Statistical Strategies for Small Sample Research, pp. 307-341. Thousand Oaks (CA): Sage Publications.
    9. Chin, W. W. 1998. Issues and Opinion on Structural Equation Modeling. MIS Quarterly, 22 (1), vii-xvi.
    10. Chukwunedu, O. S. & Okoye, E. I. 2012. Forensic Accounting and Audit Expectation Gap - The Perception of Accounting Academics. SSRN Electronic Journal. DOI: 10.2139/ssrn.1920865. Go to original source...
    11. Cortés, D., Santamaría, J. & Vargas, J. F. 2016. Economic Shocks and Crime: Evidence from the Crash of Ponzi Schemes. Journal of Economic Behavior and Organization, 131 (A), 263-275. DOI: 10.1016/j.jebo.2016.07.024. Go to original source...
    12. Dewi, G. A. K. R. S. 2017. Pengaruh Moralitas Individu Dan Pengendalian Internal Pada Kecurangan Akuntansi (Studi Eksperimen pada Pemerintah Daerah Provinsi Bali). Jurnal Ilmiah Akuntansi, 1 (1), 77-92. DOI: 10.23887/jia.v1i1.9984. Go to original source...
    13. Dong, R.-Z. 2011. Research on Legal Procedural Functions of Forensic Accounting. Energy Procedia, 5, 2147-2151. DOI: 10.1016/j.egypro.2011.03.371. Go to original source...
    14. Fornell, C. & Larcker, D. F. 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18 (1), 39-50. DOI: 10.2307/3151312. Go to original source...
    15. Gepp, A., Linnenluecke, M. K., O'Neill, T. J. & Smith, T. 2018. Big Data in Accounting and Finance: A Review of Influential Publications and a Research Agenda. Journal of Accounting Literature, 40, 102-115. DOI: 10.2139/ssrn.2930767. Go to original source...
    16. Gupta, P. K. & Gupta, S. 2015. Corporate Frauds in India - Perceptions and Emerging Issues. Journal of Financial Crime, 22 (1), 79-103. DOI: 10.1108/JFC-07-2013-0045. Go to original source...
    17. Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. 2010. Multivariate Data Analysis: A Global Perspective. 7th ed. Pearson. ISBN 0135153093.
    18. Hassink, H., Meuwissen, R. & Bollen, L. 2010. Fraud Detection, Redress and Reporting by Auditors. Managerial Auditing Journal, 25 (9), 861-881. DOI: 10.1108/02686901011080044. Go to original source...
    19. Hull, R., Walker, R. & Kwak, S. 2013. IPO Valuation and Insider Manipulation of R&D. Managerial Finance, 39 (10), 888-914. DOI. 10.1108/MF-05-2012-0125. Go to original source...
    20. IAASB (International Auditing and Assurance Standards Board). 2009. Implementation and Inovation [online]. Available at: https://www.ifac.org/system/files/publications/files/2009_IAASB_Annual_Report.pdf.
    21. Issa, H. & Kogan, A. 2014. A Predictive Ordered Logistic Regression Model as a Tool for Quality Review of Control Risk Assessments. Journal of Information Systems, 28 (2), 209-229. DOI: 10.2308/isys-50808. Go to original source...
    22. Janney, J. J. & Gove, S. 2017. Firm Linkages to Scandals via Directors and Professional Service Firms: Insights from the Backdating Scandal. Journal of Business Ethics, 140, 65-79. DOI: 10.1007/s10551-015-2662-9. Go to original source...
    23. Jory, S. R., Ngo, T. N., Wang, D. & Saha, A. 2015. The Market Response to Corporate Scandals Involving CEOs. Applied Economics, 47 (17), 1723-1738. DOI: 10.1080/00036846.2014.995361. Go to original source...
    24. Kenny, M. 1985. The Human Factor. CALICO Journal, 3 (4), 3-5. Go to original source...
    25. Krahel, J. P. & Titera, W. R. 2015. Consequences of Big Data and Formalization on Accounting and Auditing Standards. Accounting Horizons, 29 (2), 409-422. DOI: 10.2308/acch-51065. Go to original source...
    26. Krambia-Kapardis, M. 2002. A Fraud Detection Model: A Must for Auditors. Journal of Financial Regulation and Compliance, 10 (3), 266-278. DOI: 10.1108/13581980210810256. Go to original source...
    27. Mittal, P. 2020a. A Multi-Criterion Decision Analysis Based on PCA for Analyzing the Digital Technology Skills in the Effectiveness of Government Services. In 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 490-494. DOI: 10.1109/DASA51403.2020.9317241. Go to original source...
    28. Mittal, P. 2020b. Big Data and Analytics: A Data Management Perspective in Public Administration. International Journal of Big Data Management, 1 (2), 152-165. DOI: 10.1504/ijbdm.2020.10032871. Go to original source...
    29. Moffitt, K. C. & Vasarhelyi, M. A. 2013. AIS in an Age of Big Data. Journal of Information Systems, 27 (2), 1-19. DOI: 10.2308/isys-10372. Go to original source...
    30. Morris, B. W. 2010. Forensic and Investigative Accounting (Book Review). The International Journal of Accounting, 45 (4), 496-499. DOI: 10.1016/j.intacc.2010.09.007. Go to original source...
    31. Mouawi, R., Elhajj, I. H., Chehab, A. & Kayssi, A. 2019. Crowdsourcing for Click Fraud Detection. EURASIP Journal on Information Security, 11, 1-18. DOI: 10.1186/s13635-019-0095-1. Go to original source...
    32. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y. & Sun, X. 2011. The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. Decision Support Systems, 50 (3), 559-569. DOI: 10.1016/j.dss.2010.08.006. Go to original source...
    33. Nickson, R. & Neikirk, A. 2018. Reducing the Expectation Gap. In Managing Transitional Justice: Expectations of International Criminal Trials, Chapter 7, pp. 171-222. DOI: 10.1007/978-3-319-77782-5_7. Go to original source...
    34. Okafor, C. A. & Otalor, J. I. 2013. Narrowing the Expectation Gap in Auditing: The Role of the Auditing Profession. Research Journal of Finance and Accounting, 4 (2), 43-52.
    35. Patterson, L. A. & Koller, C. A. 2011. Diffusion of Fraud Through Subprime Lending: The Perfect Storm. Sociology of Crime Law and Deviance, 16, 25-45. DOI: 10.1108/S1521-6136(2011)0000016005. Go to original source...
    36. Raguseo, E. 2018. Big Data Technologies: An Empirical Investigation on Their Adoption, Benefits and Risks for Companies. International Journal of Information Management, 38 (1), 187-195. DOI: 10.1016/j.ijinfomgt.2017.07.008. Go to original source...
    37. Reurink, A. 2018. Financial Fraud: a Literature Review. Journal of Economic Surveys, 32 (5), 1292-1325. DOI: 10.1111/joes.12294. Go to original source...
    38. Rezaee, Z. & Burton, E. J. 1997. Forensic Accounting Education: Insights from Academicians and Certified Fraud Examiner Practitioners. Managerial Auditing Journal, 12 (9), 479-489. DOI: 10.1108/02686909710185206. Go to original source...
    39. Rezaee, Z. & Wang, J. 2019. Relevance of Big Data to Forensic Accounting Practice and Education. Managerial Auditing Journal, 34 (3), 268-288. DOI: 10.1108/MAJ-08-2017-1633. Go to original source...
    40. Ruankaew, T. 2013. The Fraud Factors. International Journal of Management and Administrative Sciences, 2 (2), 1-5.
    41. Ruhnke, K. & Schmidt, M. 2014. The Audit Expectation Gap: Existence, Causes, and the Impact of Changes. Accounting and Business Research, 44 (5), 572-601. DOI: 10.1080/00014788.2014.929519. Go to original source...
    42. Salehi, M. 2016. Quantifying Audit Expectation Gap: A New approach to Measuring Expectation Gap. Zagreb International Review of Economics & Business, 19 (1), 25-44. DOI: 10.1515/zireb-2016-0002. Go to original source...
    43. Sharma, A. & Pulliam, S. 2009. Galleon Case Prompts Firms to Plug Leaks: Intel Assures Clearwire on Confidential Data; Google Cuts Ties with Investor-Relations Firm. The Wall Street Journal [online]. Available at: https://www.wsj.com/articles/SB125623097383901687.
    44. Shrout, P. E. & Bolger, N. 2002. Mediation in Experimental and Nonexperimental Studies: New Procedures and Recommendations. Psychological Methods, 7 (4), 422-445. DOI: 10.1037/1082-989X.7.4.422. Go to original source...
    45. Vasarhelyi, M. A., Kogan, A. & Tuttle, B. M. 2015. Big Data in Accounting: An Overview. Accounting Horizons, 29 (2), 381-396. DOI: 10.2308/acch-51071. Go to original source...
    46. Westland, J. C. 2007. Confirmatory Analysis with Partial Least Squares Confirmatory Analysis with Partial Least Squares. University of Science & Technology, Clearwater Bay, Kowloon, Hong Kong.
    47. Williams, I. 2006. Real Scandal Not Oil-for-Food, but CPA-Administered Development Fund for Iraq. Washington Report on Middle East Affairs [online]. Available at: https://www.wrmea.org/006-march/real-scandal-not-oil-for-food-but-cpa-administered-development-fund-for-iraq.html.
    48. Wold, S., Ruhe, A., Wold, H. & Dunn, W. J. 1984. The Collinearity Problem in Linear Regression: The Partial Least Squares (PLS) Approach to Generalized Inverses. SIAM Journal on Scientific and Statistical Computing, 5 (3), 735-743. DOI: 10.1137/0905052. Go to original source...

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