WHEN TECHNOLOGY MATURITY MATTERS: A MODERATED REGRESSION STUDY OF FORENSIC ANALYTICS AND FRAUD DETECTION
Keywords:
Data‐driven forensic analytics, technological maturity, fraud detection effectiveness, moderation, PLS‐SEM, emerging economy, boundary conditionAbstract
Purpose: Data‐driven forensic analytics (DDFA) is widely promoted as a key tool for fraud detection, yet organisations report mixed results. This study investigates whether the effectiveness of DDFA depends on the organisation’s technological maturity – defined asthe sophistication of IT infrastructure, data governance, and analytical skills. Design/Methodology/Approach: A two‐wave longitudinal survey was conducted with 210 finance and audit professionals in a large emerging economy; 178 completed both waves (retention 84.8%). Data were analysed using PLS‐SEM (SmartPLS 4.0), including measurement model assessment (CFA, AVE, HTMT), structural path analysis, and moderation testing via the product indicator approach with 5,000 bootstrap resamples. Endogeneity, common method bias, and non‐response bias were rigorously tested. Phase 2 semi‐structured interviews with 15 audit directors provided qualitative validation. Findings: DDFA has a significant positive association with fraud detection effectiveness (FDE) (β = 0.39, p < 0.001). Technological maturity significantly moderates this relationship (interaction β = 0.22, p = 0.001). Simple slope analysis shows that the DDFA‐FDE association is strong and significant at high technological maturity (β = 0.48, p < 0.001) but weak and non‐significant at low maturity (β = 0.19, p = 0.08). The model explains 61% of variance in FDE (R2 = 0.61). Qualitative interviews confirm that immature infrastructures – poor data quality, legacy systems, and lack of analytical skills – severely limit the value of forensic analytics. Practical Implications: Organisations should not invest in advanced forensic analytics without simultaneously upgrading their technological base. A practical maturity assessment tool is provided. Investments in data governance, system integration, and analytical skills are prerequisites, not complements. Originality/Value: This study is the first to empirically demonstrate technological maturity as a boundary condition for analytics effectiveness in fraud detection, resolving conflicting prior findings. It extends the Information Systems Success Model by introducing infrastructure quality as a key moderator.
References
1. Appelbaum D, Kogan A, Vasarhelyi MA. Big data and analytics in the modern audit engagement: Research needs. Audit J Pract Theory. 2017;36(4):1–27.
2. Perols J. Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Audit J Pract Theory. 2011;30(2):19–50.
3. Bao Y, Ke B, Li B, Yu Y, Zhang J. Detecting accounting fraud in publicly traded U.S. firms using a machine learning approach. J Account Res. 2020;58(1):199–235.
4. Ransbotham S, Kiron D, Prentice PK. Beyond the hype: Analytics in the audit. MIT Sloan Manag Rev. 2021;62(3):45–52.
5. Earley CE. Data analytics in auditing: Opportunities and challenges. Bus Horiz. 2015;58(5):493–500.
6. Dechow PM, Ge W, Larson CR, Sloan RG. Predicting material accounting misstatements. Contemp Account Res. 2011;28(1):17–82.
7. Ge W, Koester A, McVay S. Benefits and costs of Sarbanes-Oxley section 404 compliance. Account Rev. 2022;97(4):217–242.
8. Brown-Liburd H, Issa H, Lombardi D. Behavioral implications of big data’s impact on audit judgment and decision making. Account Horiz. 2015;29(2):451–468.
9. Beneish MD. The detection of earnings manipulation. Financ Anal J. 1999;55(5):24–36.
10. Issa H, Kogan A. A predictive model for financial statement fraud detection. J Emerg Technol Account. 2014;11(2):145–165.
11. Zhang J, Zhou Y. Detecting financial fraud using machine learning: A survey. IEEE Access. 2019;7:159667–159688.
12. Singleton T, Singleton A. Fraud auditing and forensic accounting. 4th ed. Hoboken: Wiley; 2010.
13. Jans M, Alles M, Vasarhelyi MA. A field study on the use of process mining of event logs as an analytical procedure in auditing. Account Rev. 2014;89(5):1751–1773.
14. Kotsiantis S, Zaharakis I, Pintelas P. Machine learning: A review of classification and combining techniques. Artif Intell Rev. 2006;26(3):159–190.
15. DeLone WH, McLean ER. The DeLone and McLean model of information systems success: A ten- year update. J Manag Inf Syst. 2003;19(4):9–30.
16. Luftman J. Assessing business-IT alignment maturity. Commun Assoc Inf Syst. 2000;4(14):1–50.
17. Jensen MC, Meckling WH. Theory of the firm: Managerial behavior, agency costs and ownership structure. J Financ Econ. 1976;3(4):305–360.
18. Barney J. Firm resources and sustained competitive advantage. J Manag. 1991;17(1):99–120.
19. West J, Bhattacharya M. Intelligent financial fraud detection: A comprehensive review. Comput Secur. 2016;57:47–66.
20. Wells JT. Corporate fraud handbook. 5th ed. Hoboken: Wiley; 2017.
21. Nolan RL. Managing the computer resource: A stage hypothesis. Commun ACM. 1973;16(7):399–405.
22. Sutton SG. Continuous auditing: An overview. J Emerg Technol Account. 2006;3(1):1–11.
23. Knechel WR. Auditing: Assurance and risk. 3rd ed. London: Routledge; 2016.
24. JE, Wright AM, Wright S. Continuous reporting and continuous assurance: Implications for internal auditing. J Inf Syst. 2014;28(2):165–183.
25. Alles MG. Drivers of the use and facilitators of the adoption of continuous auditing. Int J Account Inf Syst. 2015;16:1–22.
26. Bierstaker JL, Brody RG, Pacini C. Accountants’ perceptions regarding fraud detection and prevention methods. Manag Audit J. 2006;21(5):520–535.
27. Ringle CM, Wende S, Becker JM. SmartPLS 4. Oststeinbek: SmartPLS GmbH; 2022.
28. Dorminey J, Fleming AS, Kranacher MJ, Riley RA. The evolution of fraud theory. Issues Account Educ. 2012;27(2):555–579.
29. Committee of Sponsoring Organizations (COSO). Internal control—Integrated framework. New York: COSO; 2013.
30. Alles MG, Brennan G, Kogan A, Vasarhelyi MA. Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens. Int J Account Inf Syst. 2006;7(2):137–161.
31. Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. 3rd ed. Thousand Oaks: Sage; 2017.
32. Hogan CE, Rezaee Z, Riley RA, Velury UK. Financial statement fraud: Insights from the academic literature. Audit J Pract Theory. 2008;27(2):231–252.
33. Romney MB, Steinbart PJ. Accounting information systems. 14th ed. Boston: Pearson; 2018.
34. King N. Doing template analysis. In: Symon G, Cassell C, editors. Qualitative organizational research: Core methods and current challenges. London: Sage; 2012. p. 426–450.
35. Vasarhelyi MA, Alles MG, Williams KT. Continuous assurance for the now economy. Strateg Financ. 2010;92(4):39–45.
36. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance- based structural equation modeling. J Acad Mark Sci. 2015;43(1):115–135.
