What is the key determinant of the credit card fraud risk assessment in Indonesia? An idea for brainstorming
-
DOIhttp://dx.doi.org/10.21511/bbs.18(1).2023.03
-
Article InfoVolume 18 2023, Issue #1, pp. 26-37
- Cited by
- 846 Views
-
362 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This study examined the direct effect of brainstorming on fraud risk assessment at credit card issuing banks in Indonesia. Therefore, it was expected to help improve their performance in dealing with various credit card frauds. This study involved 80 participants from the credit card fraud risk management team from four major credit card issuing banks in Indonesia, consisting of the risk management team (anti-fraud specialist) and the internal auditor team. The research was analyzed using the experimental method with a 2X1 factorial design. Analysis of Variance (ANOVA) would test the experimental data. The individuals’ performance (without brainstorming) or the brainstorming group was analyzed using the statistical ANOVA technique. ANOVA analysis produced a sig value of less than 1% and an F-count of 50.556 > 0.143443, which was higher than the F-table. The ANOVA test results concluded that there were differences in assessing the fraud between the respondents with brainstorming and those without it. Through the brainstorming method, it turned out that the respondents in the fraud risk management team provided a more accurate credit card fraud risk assessment from the point of view of the fraud causes and the credit card fraud impacts.
Hence, it is crucial for credit card issuing banks in Indonesia to consistently implement anti-fraud governance by adopting brainstorming to produce a better fraud risk assessment.
Acknowledgments
This research was conducted as part of the process of study completed at the University of Padjadjaran, Bandung, Indonesia. Very special thanks to the participants from major credit card issuer banks who have participated in this research.
- Keywords
-
JEL Classification (Paper profile tab)D18, G21, G32
-
References48
-
Tables8
-
Figures4
-
- Figure 1. Normal line graph (non-brainstorming group)
- Figure 2. Normal line graph (brainstorming group)
- Figure 3. Normality histogram (non-brainstorming group)
- Figure 4. Normality histogram (brainstorming group)
-
- Table 1. Factorial design
- Table 2. Demographic statistics
- Table 3. Composition of the factorial design
- Table 4. Descriptive statistics of the experiment participants’ response
- Table 5. Reasons for fraud risk assessment
- Table 6. Normality test results
- Table 7. Homogeneity test
- Table 8. ANOVA results for assessing the fraud risk management team participants
-
- Agwu, E. (2018). Reputational risk impact of internal frauds on bank customers in Nigeria. SSRN Electronic Journal.
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
- Albrecht, C., Holland, D., Skousen, B., & Skousen, C. (2018). The significance of whistleblowing as an anti-fraud measure. Journal of Forensic and Investigative Accounting, 10(1), 1-13.
- Alon, A., & Dwyer, P. (2010). The impact of groups and decision aid reliance on fraud risk assessment. Management Research Review, 33(3), 240–256.
- Arens, A. A., Elder, R. J., Beasley, M. S., & Hogan, C. E. (2019). Auditing and assurance services (15th ed.). Pearson.
- Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behavior: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499.
- Arthur, J. B., & Huntley, C. L. (2005). Ramping up the organizational learning curve: assessing the impact of deliberate learning on organizational performance under gainsharing. Academy of Management Journal, 48(6), 1159-1170.
- Association of Certified Fraud Examinrs. (2018). Report to The Nations: 2018 Global Study On Occupational Fraud and Abuse. Japan: Asia Pacific Edition.
- Beasley, M. S., & Jenkins, J. G. (2003). A primer for brainstorming fraud risks. Journal of Accountancy.
- Bock, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111.
- Boritz, J. E., Kochetova-Kozloski, N., & Robinson, L. (2014). Are fraud specialists relatively more effective than auditors at modifying audit programs in the presence of fraud risk? The Accounting Review, 90(3), 881-915.
- Borthick, A. F., Curtis, M. B., & Sriram, R. S. (2006). Accelerating the acquisition of knowledge structure to improve performance in internal control reviews. Accounting, Organizations and Society, 31(4-5), 323-342.
- Boyle, D., DeZoort, F., & Hermanson, D. (2015). The effects of internal audit report type and reporting relationship on internal auditors’ risk judgment. Accounting Horizons, 29(3), 395-718.
- Brazel, J. F., Carpenter, T. D., & Jenkins, J. G. (2010). Auditors’ Use of brainstorming in the consideration of fraud: Reports from the Field. The Accounting Review, 85(4), 1273-1301.
- Byron, K. (2012). Creative reflections on brainstorming. London Review of Education. 10(2), 2013.
- Carpenter, T. D. (2007). Audit team brainstorming, fraud risk identification, and fraud risk assessment: Implications of SAS no. 99. The Accounting Review, 82(5), 1119-1140.
- Carpenter, T., Reimers, J. L., & Fretwell, P. Z. (2008). Internal auditors’ fraud risk assessments: the benefits of brainstorming in groups. Auditing: A Journal of Practice & Theory, 30(3), 211-224. SSRN Electronic Journal.
- Chui, L., Mary B. C., & Byron, J. P. (2022). How does an audit or a forensic perspective influence auditors’ fraud-risk assessment and subsequent risk response? Auditing: A Journal of Practice & Theory, 41(4), 57-83.
- Dar, H., Abbasi, A., & Naveed, A. (2020). Credit card fraud prevention planning using fuzzy cognitive maps and simulation. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 289-294).
- De Dreu, C. K. W. (2007). Cooperative outcome interdependence, task reflexivity, and team effectiveness: A motivated information processing perspective. Journal of Applied Psychology, 92(3), 628-638.
- Diehl, M., & Stroebe, W. (1987). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology, 53(3), 497-509.
- Dionne, G., Fluet, C., & Desjardins, D. (2007). Predicted risk perception and risk-taking behavior: The case of impaired driving. Journal of Risk and Uncertainty, 35(3), 237-264.
- Erjavec, N. (2011). Tests for homogeneity of variance. International Encyclopedia of Statistical Science (pp. 1595-1596).
- Fukukawa, H., & Mock, T. J. (2011). Audit risk assessments using belief versus probability. AUDITING: A Journal of Practice & Theory, 30(1), 75-99.
- Goldmann, P. D. (2015). Anti-Fraud Risk and Control Workbook. Wiley.
- Gujarati, D. N., & Porter, D. C. (2017). Basic econometrics. Mcgraw-Hill/Irwin.
- Hoffman, V. B., & Zimbelman, M. F. (2009). Do strategic reasoning and brainstorming help auditors change their standard audit procedures in response to fraud risk? The Accounting Review, 84(3), 811-837.
- Joyce, E. J., & Biddle, G. C. (2017). Are auditors’ judgments sufficiently regressive? Journal of Accounting Research, 19, 323-349.
- Kerr, D. S. (2013). Fraud-risk factors and audit planning: The effects of auditor rank. Journal of Forensic and Investigative Accounting, 5(2), 48-76.
- Kohn, N. W., & Smith, S. M. (2010). Collaborative fixation: Effects of others’ ideas on brainstorming. Applied Cognitive Psychology, 25(3), 359-371.
- Kozloski, T.M. (2011). Knowledge transfer in the fraud risk assessment task. Journal of Forensic and Investigative Accounting, 3(1), 49-85.
- Li Q., & M. Vasarhelyi. (2018). Developing a cognitive assistant for the audit plan brainstorming session. The International Journal of Digital Accounting Research, 18, 119-140.
- Lynch, A. L., Murthy, U. S., & Engle, T. J. (2009). Fraud Brainstorming using computer-mediated communication: The effects of brainstorming technique and facilitation. The Accounting Review, 84(4), 1209-1232.
- Mohd-Nassir, M.-D., Mohd-Sanusi, Z., & Ghani, E. K. (2016). Effect of brainstorming and expertise on fraud risk assessment. International Journal of Economics and Financial Issues, 6(S4), 62-67.
- Moyes, G. D., & Hasan, I. (1996). An empirical analysis of fraud detection likelihood. Managerial Auditing Journal, 11(3), 41-46.
- Mubako, G. (2012). The effects of contrasts in account-level fraud risk assessments on auditors’ evidence evaluation (Doctoral Thesis).
- Mubako, G., & O’Donnell, E. (2018). Effect of fraud risk assessments on auditor skepticism: Unintended consequences on evidence evaluation. International Journal of Auditing, 22(1), 55-64.
- O’Donnell, E., Arnold, V., & Sutton, S. G. (2000). An analysis of the group dynamics surrounding internal control assessment in information systems audit and assurance domains. Journal of Information Systems, 14(s-1), 97-116.
- Omar, N., & Din, H. F. M. (2010), Fraud diamond risk indicator: An assessment of its importance and usage. In International Conference on Science and Social Research (pp. 607-612).
- Payne, E. A., & Ramsay, R. J. (2005). Fraud risk assessments and auditors’ professional skepticism. Managerial Auditing Journal, 20(3), 321-330.
- Smith, M., Haji Omar, N., Iskandar Zulkarnain Sayd Idris, S., & Baharuddin, I. (2005). Auditors’ perception of fraud risk indicators. Managerial Auditing Journal, 20(1), 73-85.
- Sorournejad, S., Zojaji, Z., Atani, R. E., & Monadjemi, A. H. (2016). A survey of credit card fraud detection techniques: data and technique-oriented perspective.
- Stasser, G. (1999). The uncertain role of unshared information in collective choice. In Shared Cognition in Organizations (Chapter 3).
- Suman, & Nutan. (2013). Review paper on credit card fraud detection. International Journal of Computer Trends and Technology (IJCTT), 4(7), 2206-2215.
- Tang, Jiali (Jenna) and Karim, Khondkar E., DBA, C. (2017). Big Data in Business Analytics: Implications for The Audit Profession. The CPA Journal.
- Trivedi, N. K., Kumar, U., & Sharma, S. K. (2020). An efficient credit card fraud detection model based on machine learning methods. International Journal of Science and Technology, 29(5), 3414-3424.
- Vona, L. W., (2017). Fraud data analytics methodology: the fraud scenario approach to uncovering fraud in core business systems. J. Wiley & Sons.
- Yogi Prabowo, H. (2012). A better credit card fraud prevention strategy for Indonesia. Journal of Money Laundering Control, 15(3), 267-293.