Time trend analysis is an effective method in data-driven fraud detection that concentrates on assessing the historical development of financial and operational data (Albrecht et al., 2006). Through the examination of historical patterns, analysts might detect typical trends shaped by changes in seasons, economic cycles, or market conditions (Boța-Avram, 2024). In typical situations, lawful business operations exhibit stable and predictable patterns, rendering time-based analysis an effective criterion for assessing transactional behavior.

This method is very effective for identifying irregular growth trends, unexpected increases, or unaccounted decreases in financial data (Fitsilis and Kalogirou, 2021). Significant deviations in transactions or account balances from previous trends, absent a plausible business rationale, may serve as possible signs of error or fraud. An unusual increase in expenses towards the conclusion of a reporting period may imply earnings management, but unexpected revenue growth during a period of low demand may signify premature or fraudulent revenue recognition.

By conducting time trend analysis, analysts can compare anticipated performance with actual outcomes identify differences necessitating further examination (du Preez et al., 2025). This strategy prioritizes behavioral patterns over individual transactions, enhancing fraud detection by uncovering systematic abnormalities that could be overlooked in conventional point-in-time analysis.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Boța-Avram C. (2024) Examining the white and dark sides of digitalisation effects on corruption: unveiling research patterns and insights for future research. The Journal of Risk Finance 25: 181-223.
  • du Preez A, Bhattacharya S, Beling P, et al. (2025) Fraud detection in healthcare claims using machine learning: A systematic review. Artificial Intelligence in Medicine 160: 103061.
  • Fitsilis F and Kalogirou V. (2021) Interoperability Legal Patterns: A concept for Greece. ACM International Conference Proceeding Series. 485-487.