Fuzzy matching is a crucial analytical method in fraud detection employed to discover records that exhibit similarity without being identical (Charizanos et al., 2024; Albrecht et al., 2006). In contrast to exact matching, which identifies only identical entries, fuzzy matching accommodates minor differences in spelling, formatting, or character sequence. This capability is particularly important when frauds deliberately introduce minor alterations in names, addresses, or identification numbers to hide criminal activity. A hypothetical vendor may be registered under a name that closely matches that of an actual supplier to avoid early identification.

Fuzzy matching, via approximate matching algorithms, can uncover hidden linkages and duplicate entities that standard approaches fail to detect (Yazdinejad et al., 2023; Vosseler, 2022). It assists in revealing instances where an individual or organization manifests under several identities within the system. This is especially proficient in identifying conflicts of interest, related-party transactions, and cooperation among employees and other entities.

Consequently, fuzzy matching markedly improves the efficacy of data-driven fraud detection. By overcoming the limitations of precise matching, it empowers researchers to identify complex patterns of manipulation and disinformation. Thus, fuzzy matching offers enhanced understanding of complex fraud strategies that exploit data anomalies to avoid detection.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Charizanos G, Demirhan H and İçen D. (2024) An online fuzzy fraud detection framework for credit card transactions. Expert Systems with Applications 252: 124127.
  • Vosseler A. (2022) Unsupervised insurance fraud prediction based on anomaly detector ensembles. Risks 10: 132.
  • Yazdinejad A, Dehghantanha A, Parizi RM, et al. (2023) Secure intelligent fuzzy blockchain framework: Effective threat detection in iot networks. Computers in Industry 144: 103801.