Effectiveness Of Digitally Driven Forensic Audit Techniques In Enhancing Fraud Detection And Prevention: An Empirical Study From The Resourcebased Perspective

The digital transformation of financial systems has intensified the complexity of fraud. It has also challenged the effectiveness of traditional auditing approaches. Fraud schemes are now more dynamic, technology-driven, and difficult to detect using conventional methods. As a result, the relevance of modern digital-driven forensic accounting and auditing remains underexplored in existing literature. This study investigates fraud detection and prevention in digitally driven forensic auditing environments. It is grounded in the Resource-Based View (RBV) and adopts a hypothetico-deductive approach within a quantitative research design. Data was collected from 174 qualified auditors using convenience and purposive sampling techniques. The study develops an RBV-based framework integrating human, organisational, and technological resources. Human resources include specialised knowledge, skills, and continuous learning. Organisational resources include governance structures, forensic readiness, and cross-functional collaboration. Technological resources include AI, blockchain, and data analytics. The findings show that technological resources have the strongest effect on fraud detection and prevention. Human and organisational resources also show positive effects. However, their influence is mainly indirect through technological resources. This highlights the mediating role of digital resources in improving forensic outcomes. The study extends RBV to digital forensic auditing and shows that fraud detection effectiveness depends on integrated resource systems rather than isolated capabilities. It addresses a key gap by combining these dimensions into a single framework. It also shows that balanced capability development is more effective than relying on technology alone. Overall, it contributes an integrated RBV perspective for understanding fraud detection and prevention in digital environments.