Forensic data analytics as an information management capability for fraud detection

Abstract

Background: Fraudsters increasingly rely on electronic evidence to conceal their activities. As digital environments become more complex and organisations depend more heavily on electronic data, traditional audit methods are often insufficient for detecting fraud.
Objectives: This study investigates how technology-based audit tools, particularly generalised audit software and data analytics techniques, are applied to detect fraud in a petrochemical company.
Method: A qualitative design was employed, combining a literature review with a single-case study of 23 forensic audit engagements conducted between 2024 and 2025. The data were coded thematically and organised into analytical categories.
Results: The findings highlighted that both proactive and reactive forensic data analytics (FDA) approaches are complementary in fraud detection, enhancing internal controls and providing assurance services.
Conclusion: Rapid advances in technology, data and telecommunications have enabled increasingly sophisticated forms of economic crime. In this context, forensic auditing supported by data analytics has become an essential fraud-detection technique.
Contribution: This study offers practitioners a structured and standardised approach for applying FDA to improve the effectiveness of organisational fraud detection.