Bridging the expectation performance gap in fraud detection: A systematic review of external auditors' responsibilities, audit failures, and regulatory responses

Abstract

Financial statement fraud remains a persistent threat to the integrity of global capital markets despite extensive auditing standards and regulatory reforms. This study systematically reviews literature on external auditors' responsibilities in fraud detection and evaluates why significant audit failures continue to occur. Following the Preferred Reporting Items for Systematic Reviews and Meta Analyses framework, 1,232 studies were initially identified from Google Scholar and Crossref, of which 32 studies published between 2002 and 2025 met the inclusion criteria. The findings reveal a persistent expectation and performance gap between auditing standards, particularly ISA 240, and actual audit practice. Although auditors are required to obtain reasonable assurance that financial statements are free from material misstatement due to fraud, high profile corporate failures such as Enron, Wirecard, Carillion and Steinhoff demonstrate recurring deficiencies in fraud detection. The synthesis identifies four dominant factors influencing audit effectiveness: regulatory environment, auditor independence, professional competence and scepticism, and the complexity of fraud schemes. The review further shows that audit failures are driven not only by technical limitations but also by behavioural biases, institutional pressures and increasing fraud sophistication. Regulatory reforms such as the Sarbanes Oxley Act and CLERP 9 have improved compliance but have not eliminated audit deficiencies. The study contributes a multidimensional conceptual framework integrating behavioural, institutional and technological perspectives to explain persistent audit failures. It concludes that improving fraud detection requires a shift from compliance based auditing toward a more integrated approach that combines behavioural insight, enhanced professional training and advanced audit technologies such as artificial intelligence and data analytics.