The next step after identifying fraud signs is to use technology to collect appropriate information that can show whether or not those symptoms are present within the company (Albrecht et al., 2006; Kuzuno and Yamauchi, 2020). Conceptual red flags are now converted into targeted data searches intended to gather proof from various information sources. Technology is essential because it makes it possible to access huge amounts of data that would be difficult to analyze by hand.

Accounting systems and ERP databases, which hold comprehensive records of transactions related to revenue, procurement, payroll, and financial reporting, are important data sources (Al-Jabri and Roztocki, 2015). Transaction logs also offer useful details about timing, user activity, and system overrides, which aid analysts in identifying unusual patterns of behavior. By enabling cross-checks for duplicate identities, related parties, or conflicts of interest, external datasets—such as vendor master files, employee records, or third-party databases—further improve analysis.

Data may be effectively and methodically extracted, manipulated, and validated using technological instruments (Alexopoulos et al., 2012). This guarantees data accuracy, consistency, and completeness prior to analysis. By using technology, fraud detection moves from a small-scale human assessment to a large-scale, data-driven analysis, increasing the efficacy and dependability of spotting possible signs of fraud detection.

References:

  • Al-Jabri IM and Roztocki N. (2015) Adoption of ERP systems: Does information transparency matter? Telematics and Informatics 32: 300-310.
  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Alexopoulos C, Loukis E, Charalabidis Y, et al. (2012) A Methodology for Evaluating PSI E-infrastructures Based on Multiple Value Models. Informatics (PCI), 2012 16th Panhellenic Conference on. IEEE, 37-43.
  • Kuzuno H and Yamauchi T. (2020) Identification of Kernel Memory Corruption Using Kernel Memory Secret Observation Mechanism. Ieice Transactions on Information and Systems E103D: 1462-1475.