Audit analytics (AA) enables the improvement of the effectiveness and efficiency of the internal audit function (IAF). In this research, we follow Lambrecht et al.’s (2011, p.3) and define AA as “the process of identifying, gathering, validating, analyzing, and interpreting data using information and communication technology to further the purpose and mission of internal auditing”. AA is an umbrella term encompassing various forms of technology-based audit approaches, from continuous auditing to advanced machine learning. Due to the increased effectiveness and efficiency, AA enables the IAF to provide proactive and ongoing assurance services instead of periodic assessment, expand the (internal) audit service areas with the same resources, enable larger samples or even the whole population for analysis, and can provide insight and foresight for its stakeholders, in addition to the hindsight of the (possibly) already occurred risks...

Internal audit function (IAF) effectiveness can be improved by embracing Audit Analytics (AA). However, despite its promises, AA implementation remains limited. Although there is research on AA implementation in general, there needs to be an overview of insight into inhibiting and driving factors for internal auditing. This paper examines those driving and inhibiting factors by exploring the literature on AA implementation. The initial search revealed 98 uniquely identified papers. Further filtering and the additional search returned 42 articles, which were analyzed in detail. The analysis resulted in 12 driving and 23 inhibiting factors, grouped into internal, regulation, data, infrastructure, and audit practice categories. The literature shows that IAF encounters multiple and intertwined factors in AA implementation and needs to anticipate those factors. Moreover, AA implementation affects IAF’s parts and stakeholders differently, requiring internal and external collaboration. Building on these insights, we provide recommendations for further research.

The transformation toward the use of data analytics requires overcoming many challenges. Nevertheless, the interconnections between the challenges are unclear. Gaining knowledge about these interconnections is important to prioritize strategies that aim to stimulate the transformation. This paper unravels the relationship among Audit Analytics (AA) implementation challenges to transform the Internal Audit Function (IAF) using Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) – Interpretative Structural Modelling (ISM) (or MICMAC-ISM) to develop a hierarchical model and determine the relationships among the challenges and the degree of power of each challenge. We collect data from internal auditors experienced in using audit analytics. They suggest that cultural challenges, along with technical challenges, are critical for enabling transformation. Moreover, combinations of approaches are required to address the complex interrelationships among challenges to initiate transformation. The analysis suggests that AA implementation requires a top-down approach to address cultural challenges blended with a bottom-up strategy to overcome technical challenges.