A Hybrid Approach in Contemporary Auditing: A Literature Synthesis on the Integration of Risk-Based Audit and Data Analytics

Abstract

The advancement of digital technology has fundamentally transformed auditing practice by increasing the volume and complexity of data that auditors must process, necessitating more adaptive and risk-sensitive approaches. Risk-based audit (RBA) and data analytics have emerged as two pivotal responses to this challenge; however, their individual roles and the implications of their integration for audit risk assessment remain insufficiently synthesized in the existing literature. This study aims to analyze the roles of RBA and data analytics, as well as their integration in supporting audit risk assessment. A Systematic Literature Review (SLR) guided by the PRISMA 2020 framework was employed, drawing on 30 peer-reviewed articles published between 2021 and 2026, sourced from Scopus, Web of Science, and Google Scholar, and analyzed using thematic analysis. The findings reveal that RBA improves audit process quality by enabling systematic risk prioritization and strengthening the audit function as a strategic organizational mechanism, while data analytics extends audit coverage through full population testing, enhances fraud and anomaly detection, and facilitates continuous auditing, despite persistent adoption challenges including regulatory barriers, skills gaps, and cognitive risks. The integration of both approaches optimizes risk assessment through five complementary mechanisms: expanding risk identification, improving misstatement risk assessment accuracy, enabling objective high-risk area determination, enhancing audit judgment quality, and optimizing risk-based resource allocation. These findings underscore that the successful integration of RBA and data analytics depends not solely on technological adoption, but equally on governance maturity, human resource capacity, and the development of adaptive audit standards suited to the demands of the digital era.