Stratification and summarization are crucial analytical methods in data-driven fraud detection, since they facilitate the transformation of large, complex datasets into structured and comprehensible information (Albrecht et al., 2006). Stratification functions by categorizing transactions according to specified parameters, like transaction value, time frame, vendor, employee, or department (Blau and Moore, 2018). This organized categorization allows analysts to examine data across several risk levels and immediately pinpoint regions that break from standard operational patterns. Grouping payments by amount can uncover unusual clustering within certain value ranges, potentially signaling efforts to circumvent approval constraints.

Summarization improves this process by producing aggregate measurements, such as totals, averages, frequencies, and percentages for each stratified group (Kontou and Bagos, 2024). These summary data explain transactional behavior and facilitate the comparison of patterns between groups . Irregular outcomes, such as a sole vendor obtaining an excessively large share of total payments or an individual handling an unusually high volume of transactions, becomes more obvious through summary. This enables analysts to go beyond individual transactions and concentrate on behavioral patterns.

Stratification and summarization transform raw data into significant analytical insights that facilitate effective fraud detection. Instead of evaluating transactions individually, analysts can focus on high-risk portions for additional testing and inquiry. Consequently, these strategies augment analytical efficiency, refine risk-based decision-making, and establish a robust platform for more sophisticated fraud detection methodologies.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Blau PM and Moore OD. (2018) The process of stratification. Inequality. Routledge, 1-6.
  • Kontou PI and Bagos PG. (2024) The goldmine of GWAS summary statistics: a systematic review of methods and tools. BioData Mining 17: 31.