Two Major Approaches to Fraud Detection

Albrecht et al. categorise fraud detection into two principal methodologies: passive detection and active detection. Passive detection occurs when fraud is revealed unexpectedly, often through external reports, complaints, accidental findings, or disclosures. It depends significantly on human observation instead of systematic procedures. Although not systematic, passive detection is a prevalent method for identifying fraud initially, particularly through whistleblowing or reports from workers, customers, or vendors who observe anomalies.

Active detection, in contrast, refers to organised and systematic initiatives specifically designed to identify fraud. This method employs systematic procedures like data analytics, targeted forensic audits, unplanned audits, ongoing monitoring, and formal fraud risk assessments. These strategies help firms proactively identify anomalies and red flags within financial records, digital transactions, and operational procedures. Active detection is considered more efficacious as it reduces the duration that fraud remains undetected and enhances an organisation’s capacity for a rapid response.

Passive and aggressive detection combined form a thorough fraud detection method. Passive approaches facilitate the detection of fraud that arises from chance or observation, but active methods guarantee that the organisation does not depend exclusively on chance or informants. By combining both methodologies, companies can establish a resilient detection framework—one that continually surveils for fraud, promotes reporting, and employs technological and investigative tools to identify suspicious behaviours promptly. This equitable approach enhances transparency, mitigates risk, and promotes improved governance standards.

References
Albrecht, W. S., Albrecht, C. C., Albrecht, C. O., & Zimbelman, M. F. (2019). Fraud Examination (6th ed.). Cengage Learning.