Data analysis software plays a critical role in supporting data-driven fraud detection by enabling analysts to process, test, and interpret large volumes of transactional data efficiently (Gaddam et al., 2019; Albrecht et al., 2006). Spreadsheet-based analytics, statistical software, database query tools, and audit-specific software are examples of frequently used tools. From standard audit testing to sophisticated statistical modeling and pattern identification, each category fulfills distinct analytical requirements.

Software designed for audits is made to assist with fraud-related processes like gap analysis, duplicate detection, and exception reporting (Bello and Olufemi, 2024). More complex studies, like as trend analysis, hypothesis testing, and outlier detection, can be carried out by analysts using statistical tools. Spreadsheet-based analytics provide flexibility for exploratory analysis and visualization, while database query tools enable direct access to big datasets, facilitating the effective extraction and filtering of pertinent transactions.

Crucially, how methodically and suitably the technologies are used determines how effective fraud detection is, not just how sophisticated the software is (Charizanos et al., 2024). Therefore, the focus is on professional judgment, appropriate test design, and analytical thinking; technology is used to support competent fraud examination procedures rather than to replace them.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Bello OA and Olufemi K. (2024) Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer science & IT research journal 5: 1505-1520.
  • Charizanos G, Demirhan H and İçen D. (2024) An online fuzzy fraud detection framework for credit card transactions. Expert Systems with Applications 252: 124127.
  • Gaddam A, Wilkin T, Angelova M, et al. (2019) Anomaly Detection Models for Detecting Sensor Faults and Outliers in the IoT – A Survey. 13th International Conference on Sensing Technology (ICST). Macquarie Univ, Sydney, AUSTRALIA.