Transactions that are rare and drastically different from typical patterns but may be particularly vulnerable to fraud or irregularity are the focus of outlier inquiry (Khodabandehlou and Golpayegani, 2024). Outliers are important indicators in data-driven fraud detection since they might result from mistakes, odd but legal company activity, or purposeful manipulation. Analysts can reduce enormous datasets into a manageable collection of potentially risky transactions by identifying these extreme occurrences.

This step highlights how crucial it is to prioritize anomalies according to risk rather than treating every outlier identically (Dumitrescu et al., 2022). Prioritizing which anomalies require immediate attention is aided by variables such transaction value, frequency, timing, user engagement, and proximity to control thresholds. Analysts can more effectively spend investigation efforts and concentrate on problems with the biggest potential impact by ranking outliers (Zheng et al., 2020).

Importantly, in order to prevent relying too much on statistical extremeness alone, outliers must be examined within their commercial context (Albrecht et al., 2006). Because of unique conditions or operational requirements, a transaction may be statistically rare but perfectly legal. Thus, rather than being conclusive proof of fraud, outliers should be seen as a place to start an investigation, which emphasizes the importance of expert judgment and contextual analysis.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Dumitrescu B, Băltoiu A and Budulan Ş. (2022) Anomaly detection in graphs of bank transactions for anti money laundering applications. Ieee Access 10: 47699-47714.
  • Khodabandehlou S and Golpayegani AH. (2024) FiFrauD: unsupervised financial fraud detection in dynamic graph streams. ACM Transactions on Knowledge Discovery from Data 18: 1-29.
  • Zheng RC, Gu J, Jin ZJ, et al. (2020) Load forecasting under data corruption based on anomaly detection and combined robust regression. International Transactions on Electrical Energy Systems 30.