Factors influencing the effectiveness of credit card fraud prevention in Indonesian issuing banks
-
Author(s)Yuli DewiLink to ORCID Index: https://orcid.org/0000-0002-1379-8192
,
Harry SuharmanLink to ORCID Index: https://orcid.org/0000-0003-0787-0730
,
Poppy Sofia KoeswayoLink to ORCID Index: https://orcid.org/0000-0002-7064-2602
,
Nanny Dewi TanzilLink to ORCID Index: https://orcid.org/0000-0002-0224-0273
-
DOIhttp://dx.doi.org/10.21511/bbs.18(4).2023.05
-
Article InfoVolume 18 2023, Issue #4, pp. 44-60
- 298 Views
-
125 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The increase in online credit card transactions in the digital era has caused an increase in credit card cyber incidents. This is happening globally, including in Indonesia. Thus, it will affect a bank’s reputation as well as its financial losses. Therefore, optimal fraud risk management is needed in a banking effort to prevent credit card fraud. In response, this article intended to study credit card fraud prevention by examining the relationship between digital security required for customer data security; fraud brainstorming to identify process weaknesses; and compliance management to manage regulatory compliance. The next step was to test whether the anti-fraud specialist is competent to moderate this relationship. This study used a quantitative approach. This study included 27 Indonesian card issuers. Primary sources were used to collect data for this study. The primary data were analyzed using a structural equation model (SEM). The results of the study show that digital security, fraud brainstorming, and compliance management were positively and significantly related to the prevention of credit card fraud, at a significance level of 5%, the t-statistic has a numerical value of 6.161, 5.079, and 5.98 each. Furthermore, testing the moderating effect obtained t-statistic values of 7.330, 4.161, and 7.694. Competency results obtained with positive and significant influence moderate the relationship between these factors and credit card fraud prevention. These findings have policy implications for banking and government objectives in fighting credit card fraud through implementing prevention strategies.
Acknowledgments
This research was conducted as part of the process of study completion at the Padjadjaran University, Bandung, Indonesia. We would like to express our sincere gratitude to the respondents who participated in this research.
- Keywords
-
JEL Classification (Paper profile tab)G20, G21
-
References52
-
Tables8
-
Figures0
-
- Table 1. Convergent validity test
- Table 2. Reliability test
- Table 3. R-square
- Table 4. Estimates based on the structural model
- Table A1. Variable description of Digital Security
- Table A2. Variable description of Compliance Management
- Table A3. Variable description of Fraud Brainstorming
- Table A4. Variable description of Competence
-
- Abdillah, W., & Jogiyanto. (2009). Partial Least Square (PLS) Alternatif SEM.
- Al-Alawi, A. I., & Al-Bassam, S. A. (2019). Assessing the Factors of Cybersecurity Awareness in the Banking Sector. Arab Gulf Journal of Scientific Research, 37(4), 17-32.
- Auchey, D. (2020). Effective Data Analytics and Security Strategies in Internal Audit Organizations. Walden Dissertation and Doctoral Studies, 8838.
- Badadare, V. L., Patil, R. Y., & Waghmare, V. (2018). Cybersecurity need of digital era: a review. International Journal of Computer Applications, 182(22), 9-12.
- Bailey, K. (2017). A Combined Wavelet and ARIMA Approach to Predicting Financial Time Series Ken Bailey (Master’s Thesis). Dublin City University.
- 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.
- Bryman, A. (2016). Social Research Methods (5th ed.). The United States of America by Oxford University Press.
- 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.
- Chandra Sekhar, & Kumar, M. (2023). An Overview of Cyber Security in Digital Banking Sector. East Asian Journal of Multidisciplinary Research, 2(1), 43-52.
- Chaudhary, K., & Mallick, B. (2012). Exploration of Data mining techniques in Fraud Detection: Credit Card. International Journal of Electronics and Computer Science Engineering, 1(3), 1765-1771.
- Coglianese, C., & Nash, J. (2021). Compliance Management Systems: Do They Make a Difference? In B. Van Rooij & D. Sokol (Eds), The Cambridge Handbook of Compliance (pp. 571-593). Cambridge: Cambridge University Press.
- Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling Theory: A Review and Assessment. Journal of Management, 37(1), 39-67.
- Dell’Ariccia, G. (2001). Asymmetric information and the structure of the banking industry. European Economic Review, 45(10), 1957-1980.
- Dewi, Y., Suharman, H., Sofia Koeswayo, P., & Dewi Tanzil, N. (2023). What is the key determinant of the credit card fraud risk assessment in Indonesia? An idea for brainstorming. Banks and Bank Systems, 18(1), 26-37.
- Donaldson, L., & Davis, J. H. (1991). Stewardship Theory or Agency Theory: CEO Governance and Shareholder Returns. Australian Journal of Management, 16(1), 49-64.
- FST Media. (2016). The Economics of Fraud – Mitigating Risk Amidst Fast Growth and Innovation.
- Ghauri, F. (2021). Digital Security Versus Private Information. International Journal of Computer Science and Information Security (IJCSIS), 19(7), 10-20.
- Goztepe, K. (2012). Designing fuzzy rule based expert systems for cybersecurity. International Journal of Information Security Science. International Journal of Information Security Science, 1(1), 13-19.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hasham, S., Joshi, S., & Mikkelsen, D. (2019). Financial Crime and Fraud in The Age of Cybersecurity. McKinsey & Company.
- Holt, T. J., Smirnova, O., & Hutchings, A. (2016). Examining signals of trust in criminal markets online. Journal of Cybersecurity, 2(2), 137-145.
- Hovland, C. I., & Sherif, M. (1980). Social judgments: Assimilation and contrast effects in communication and attitude change. Greenwood Press.
- HSN Consultants Inc. (2021). Nilson Report 1209.
- Hubs, R. (2012). Fraud brainstorming. Planning to find fraud. Fraud Magazines, July/August, 2012. Association of Certified Fraud Examiners, Inc.
- IT Governance Indonesia (ITG.ID). (2019). Apa saja peran audit internal dalam cyber security? ITG.id.
- Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.
- Kathirvel, K. (2013). Credit Card Frauds and Measures to Detect and Prevent Them. International Journal of Marketing, Financial Services & Management Research, 2(3), 172-179.
- Kim, S.S., & Kim, Y. J. (2017). The effect of compliance knowledge and compliance support systems on information security compliance behavior. Journal of Knowledge Management, 21(4), 986-1010.
- Laurens, R., & Zou, C. C. (2016). Using Credit/Debit Card Dynamic Soft Descriptor as Fraud Prevention System for Merchant. 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-7).
- Lee, L. (2019). Cybercrime has evolved: it’s time cybersecurity did too. Computer Fraud & Security, 2019(6), 8-11.
- 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(4), 62-67.
- Myerson, R. (2002). Game theory, analysis of conflict. Harvard University Press.
- No, W. G., & Vasarhelyi, M. A. (2017). Cybersecurity and continuous assurance. Journal of Emerging Technologies in Accounting, 14(1), 1-12.
- Obuh, A. O., & Babatope, I. S. (2011). Cybercrime Regulation: The Nigerian Situation. In E. Adomi (Ed.), Frameworks for ICT Policy: Government, Social and Legal Issues (pp. 98-112). IGI Global.
- Othman, R., Aris, N. A., Mardziyah, A., Zainan, N., & Amin, N. M. (2015). Fraud Detection and Prevention Methods in the Malaysian Public Sector: Accountants’ and Internal Auditors’ Perceptions. Procedia Economics and Finance, 28, 59-67.
- Pearson, T. A., & Singleton, T. W. (2008). Fraud and Forensic Accounting in the Digital Environment. Issues in Accounting Education, 23(4), 545-559.
- Petrov, C. (2021). 20 Credit Card Fraud Statistics to Keep You Aware in 2022. Spendmenot.
- Popoola, O. M. J., Che Ahmad, A., & Samsudin, R. (2013). Forensic Accounting Knowledge and Skills on Task Performance Fraud Risk Assessment: Nigerian Public Sector Experience (MPRA Paper No. 66676).
- Posthumus, S., & Solms, R. V. (2004). A Framework for the governance of information security. Computers & Security, 23(8), 638-646.
- Quick, R., & Sayar, S. (2021). The impact of assurance on compliance management systems on bank directors’ decisions. International Journal of Auditing, 25(1), 3-23.
- Remmerbach, K., & Krumme, R. (2020). The effectiveness of compliance management systems. FH Münster University of Applied Sciences.
- Riley, B. (2007). Anti-fraud technologies: a business essential in the card industry. Card Technology Today, 19(10), 10-11.
- Schafer, B. A., & Schafer, J. K. (2019). Interpersonal Affect, Accountability and Experience in Auditor Fraud Risk Judgments and the Processing of Fraud Cues (pp. 43-65).
- Sekaran, U., & Bougie, R. (2016). Research Methods for Business: A Skill Building Approach (7th ed.). Wiley.
- Shiva, S., Roy, S., & Dasgupta, D. (2010). Game theory for cybersecurity. Proceedings of the Sixth Annual Workshop on Cybersecurity and Information Intelligence Research (pp. 1-4).
- Sidoti, P. M., & Devasagayam, R. (2010). Credit cards and college students: effect of materialism and risk attitude on misuse. The Marketing Management Journal, 20(2), 64-79.
- Szczepaniuk, E. K., & Szcepaniuk, H. (2022). Analysis of cybersecurity competencies: Recommendations for telecommunications policy. Telecommunication Policy, 46(3), 102282.
- Talamantes, J. (2020). Why is intelligence sharing important in cybersecurity? RedTeam Security.
- Vatsa, V., Sural, S., & Majumdar, A. K. (2007). A Rule-Based and Game-Theoretic Approach to Online Credit Card Fraud Detection. International Journal of Information Security and Privacy, 1(3), 26-46.
- Viswanathan, M., Yang, Y. K., Whangbo, T. K., Kim, N. B., & Garner, B. (2005). Knowledge-based compliance management systems – methodology and implementation. Fourth Annual ACIS International Conference on Computer and Information Science (ICIS’05) (pp. 25-29).
- Vousinas, G. L. (2019). Advancing theory of fraud: the S.C.O.R.E. model. Journal of Financial Crime, 26(1), 372-381.
- Yogi Prabowo, H. (2012). A better credit card fraud prevention strategy for Indonesia. Journal of Money Laundering Control, 15(3), 267-293.
-
Perceived health risk, online retail ethics, and consumer behavior within online shopping during the COVID-19 pandemic
Yuniarti Fihartini ,
Arief Helmi ,
Meydia Hassan ,
Yevis Marty Oesman
doi: http://dx.doi.org/10.21511/im.17(3).2021.02
Innovative Marketing Volume 17, 2021 Issue #3 pp. 17-29 Views: 3943 Downloads: 1525 TO CITE АНОТАЦІЯThe risk of virus contracting during the COVID-19 pandemic has changed consumer preference for online shopping to meet their daily needs than shopping in brick-and-mortar stores. Online shopping presents a different environment, atmosphere, and experience. The possibility of ethical violations is higher during online than face-to-face transactions. Therefore, this study was conducted to investigate the influence of perceived health risk and customer perception of online retail ethics on consumer online shopping behavior during the COVID-19 pandemic, involving seven variables, namely perceived health risk, security, privacy, non-deception, reliability fulfillment, service recovery, and online shopping behavior. The data were collected through an online survey by employing the purposive sampling technique to a consumer who has shopped online during the COVID-19 pandemic in Indonesia. 315 valid responses were obtained and analyzed through quantitative method using SEM-Amos. The results showed that perceived health risk and four variables of online retail ethics including security, privacy, reliability fulfillment, and service recovery affected online shopping behavior. Meanwhile, non-deception was found to have an insignificant effect. The coefficient value proved perceived health risk to be more dominant in influencing online shopping behavior than the variables of online retail ethics. Thus, consumers pay more concern for their health during online shopping. However, positive consumer perceptions of the behavior of online retail websites in providing services also can encourage consumers to shop online during this pandemic.
-
Integrated reporting and financial performance of South African listed banks
Reon Matemane ,
Rozane Wentzel
doi: http://dx.doi.org/10.21511/bbs.14(2).2019.11
Banks and Bank Systems Volume 14, 2019 Issue #2 pp. 128-139 Views: 2465 Downloads: 493 TO CITE АНОТАЦІЯThe recent development of integrated reporting intends to address the limitations associated with corporate reporting practices. This paper aims to examine whether a statistically significant relationship exists between integrated reporting quality and financial performance. Secondary data was used, namely the integrated reports and annual financial statements of South African banks listed on the Johannesburg Stock Exchange (JSE) for 2010–2014. For the period 2005–2009, only the financial statements were used, since integrated reporting was not yet mandatory. The research design was longitudinal and it combined qualitative and quantitative methods. Descriptive statistics and Feasible Generalized Least Square (FGLS) were used to explore the relationships between financial performance and integrated reporting quality. The results indicate that there is a positive relationship between integrated reporting quality (IRQ) and earnings per share (EPS). However, there is no significant relationship between IRQ and Tobin’s q (Q-Ratio), IRQ and return on equity (ROE), IRQ and return on assets (ROA) as well as IRQ and economic value added (EVA). Moreover, there are no significant differences on the financial performance of the listed banks before and after the introduction of integrated reporting.
-
Relationship between financial performances and marketing practices in the banking sector of Jordan
Majed Khalil Shami
doi: http://dx.doi.org/10.21511/bbs.14(1).2019.02
Banks and Bank Systems Volume 14, 2019 Issue #1 pp. 11-19 Views: 1641 Downloads: 237 TO CITE АНОТАЦІЯThe study aims at analyzing the relationship between financial performances and marketing practices in the banking sector of Jordan. A questionnaire was distributed to 45 top, middle, and branch level managers of 15 banks. The financial data was obtained from the financial statements and annual reports of the banks during the five-year period between 2011 and 2015. The three categories of participants, who were recruited, were top-level managers, middle-level managers and branch-level managers from 17 banks of Jordan. No two means were found to differ significantly at 0.05 level by means of Scheffe test. The results revealed that the more positive the perception was of the managers regarding the position of their banks in the market, the more they were inclined to choose an accurate target market in accomplishing their marketing objectives. Therefore, it has been concluded that when the financial needs of the customers were similar, the change in the loan-to-deposit ratio was significantly positive.
