The next step after identifying potential fraud schemes is to list potential fraud symptoms, often known as red flags (Albrecht et al., 2006; Bao et al., 2022). Detectable trends or anomalies in data that could point to the existence of fraudulent behavior are known as fraud symptoms. They are early warning indicators that call for additional analytical testing and expert examination, but they do not prove fraud.

Typical fraud signs include duplicate payments, which may signal vulnerabilities in payment controls or intentional manipulation through repeated invoices or vendor data (Alharasis et al., 2025). Unusual transaction timing, such as entries made on holidays, close to the end of a period, or outside of regular business hours, may be an attempt to hide activity or bypass regulations. When round-number entries appear frequently in accounts that typically include variable amounts, they may indicate approximations or fraudulent transactions.

Transactions that are reported slightly below approval levels are another significant indicator that could point to intentional structuring to get around authorization restrictions (Bello and Olufemi, 2024). Because these symptoms may be readily converted into analytical tests like time analysis, threshold analysis, and duplicate identification, they are crucial. Analysts establish a direct connection between data-driven testing and fraud risk assessment by methodically classifying fraud symptoms, which facilitates more targeted and efficient fraud detection.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Alharasis EE, Haddad H, Alhadab M, et al. (2025) Integrating forensic accounting in education and practices to detect and prevent fraud and misstatement: case study of Jordanian public sector. Journal of Financial Reporting and Accounting 23: 100-127.
  • Bao Y, Hilary G and Ke B. (2022) Artificial intelligence and fraud detection. Innovative Technology at the Interface of Finance and Operations: Volume I: 223-247.
  • 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.