ISSN: 2225-8329
Open access
Fraud has become the most viable threat to the global economy requiring maximum attention of forensic accountants and traditional auditors, as well as anti-graft bodies worldwide. The primary objective of this paper is to discuss the process of screening, editing and preparation of initial data collected, before any further multivariate analysis of the study regarding the relationship between fraud risk management and risk culture on bank performance. A survey method was employed to administer a total of 417 questionnaires to either the senior officer in the risk management department, internal control department, and branch manager of each bank in the Nigerian banking sector. The questionnaire is a 5 point Likert-scale. The data was analyzed using Statistical Package for the Social Sciences (SPSS) version 23 (v23). The initial data screening and cleaning were conducted as an attempt to fulfill the assumptions of multivariate analysis. Therefore, the present study assessed missing values, outliers, normality test, collinearity test, common method variance, and test of non-response bias with the help of SPSS V23. The results have shown that the data satisfied the multivariate analysis assumptions which indicate the fulfillment of conditions for further multivariate analysis.
1. Abdullahi, R., & Mansor, N. (2018). Fraud prevention initiatives in the Nigerian public sector: understanding the relationship of fraud incidences and the elements of fraud triangle theory. Journal of Financial Crime, 25(2), 527–544.
2. AbdulRasheed, A., Babaita, I. S., & Yinusa, M. A. (2012). Fraud and its implication for bank performance in Nigeria. International Journal of Asian Social Science, 2(4), 382–387.
3. ACFE. (2010). Report to the nations: On occupational fraud and abuse. United States of America.
4. Alavi, H. (2016). Mitigating the risk of fraud in documentary letters of credit. Baltic Journal of European Studies, 6(1), 139–156. https://doi.org/10.1515/bjes-2016-0006
5. Albrecht, S. W., Albrecht, C. O., Albrecht, C. C., & Zimbelman, M. F. (2012). Fraud Examination. South-Western Cengage Learning (Fourth). United States of America: South-Western, Cengage Learning.
6. Antony, J. P., & Bhattacharyya, S. (2010). Measuring organizational performance and organizational excellence of SMEs – Part 1: a conceptual framework. Measuring Business Excellence, 14(2), 3–11. https://doi.org/10.1108/13683041011047812
7. Armstrong, J. S., & Overton, T. S. (1977). Estimating Nonresponse Bias in Mail Surveys. Journal of Marketing Research, 396–402.
8. Australian Standard. Fraud and Corruption Control, AS 8001-2008 § (2008). Australia.
9. Boateng, A. A., Boateng, G. O., & Acquah, H. (2014). A literature review of fraud risk management in micro finance institutions in Ghana. Research Journal of Finance and Accounting, 5(11), 42–52.
10. Chatterjee, S., & Hadi, A. S. (2006). Regression Analysis by Example (Fourth). John Wiley & Sons, Inc. https://doi.org/10.1002/0470055464
11. Chazi, A., Khallaf, A., & Zantout, Z. (2018). Corporate governance and bank performance: Islamic versus non-islamic banks in GCC countries. The Journal of Deverloping Areas, 52(2).
12. Chernick, M. R. (2011). Bootstrap methods: A guide for practitioners and researchers (Second). Hoboken, New Jersey: John Wiley & Sons, Inc. https://doi.org/9780471756217
13. Coram, P., Ferguson, C., & Moroney, R. (2008). Internal audit, alternative internal audit structures and the level of misappropriation of assets fraud. Accounting and Finance, 48(4), 543–559. https://doi.org/10.1111/j.1467-629X.2007.00247.x
14. Coso. (2016). Summary of Internal Control-Integrated Framework by COSO?:
15. Cristian, S., & Monica, L. (2017). Measuring performance in organizations from multi-dimensional perspective Annals of the University of TâRgu Jiu, Economy Series, (4), 217–223.
16. Cruz, D. M. (2007). Application of data screening procedures in stress research. The New School Psychology Bulletin, 5(2), 41–45.
17. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly (MISQ), 39(2), 297–316.
18. Dong, Y., & Peng, C. Y. J. (2013). Principled missing data methods for researchers. SpringerPlus, 2(222), 1–17. https://doi.org/10.1186/2193-1801-2-222
19. Edge, M. E., & Sampaio, P. R. F. (2009). A survey of signature based methods for financial fraud detection. Computers and Security, 28(6), 381–394. https://doi.org/10.1016/j.cose.2009.02.001
20. Elnihewi, I. M., Mohamed, R., & Hanim, F. (2017). Contextual factors of performance measurement systems design in Libyan commercial banks. Akademia Baru, 7(2), 13–28.
21. Fidell, L. S., & Tabachnick, B. G. (2003). Preparatory data analysis. In Handbook of Psychology: Volume 2 Research Methods in Psychology (pp. 115–141). United State of America: John Wiley & Sons, Inc. https://doi.org/10.1002/0471264385.wei0205
22. Fraser, J., Simkins, B. J., & Brooks, D. W. (2010). Creating a Risk-Aware Culture. In Enterprise Risk Management (pp. 87–95). Hoboken, NJ, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/ 9781118267080.ch6
23. Ghazali, M. Z., Rahim, M. S., Ali, A., & Abidin, S. (2014). A preliminary study on fraud prevention and detection at the state and local government entities in Malaysia.
To cite this article: Hussaini, U., Bakar, A.A., Yusuf, M.-B., O. (2018). The Effect of Fraud Risk Management, Risk Culture, on the Performance of Nigerian Banking Sector: Preliminary Analysis, International Journal of Academic Research in Accounting, Finance and Management Sciences 8 (3): 224-237.
Copyright: © 2018 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode