The Role of Data Analytics in Audit Risk Assessment

Abstract

The changing business environment and technical advancements have presented new challenges for the audit industry. Audit clients have adopted data analytics to understand their business and consequently enhance their decision-making. The audit industry is usually a follower when it comes to new techniques on the market, and data analytics is not an exception. Analytical procedures are included in the traditional audit methods, but these procedures differ from the tools that data analytics provides. In the new business environment, data is generated at an accelerating pace in increasingly complex IT systems. Thus, auditors face the problem of verifying this information in a reliable way. Furthermore, this can lead to issues in audit effectivity and quality in the long run. Audit data analytics (ADA) has presented promising characteristics to aid auditors in the new era. ADA provides increased data processing capabilities, effective risk identification, possibilities to test complete populations, and support for auditors’ judgments.

The aim of this study is to discover the role of data analytics in audit risk assessment with semistructured interviews conducted with industry experts from Big Four accounting firms. The focus of the thesis is on how audit data analytics is used in risk assessment and how the use affects the audit process. Parts of grounded theory were used to analyze the empirical material. Thereafter, the results were analyzed through a conceptual framework which was derived from previous research in the field. Thereby, this study contributes to the existing literature with new findings.

This study made findings regarding the practical implementations of ADA and the use of ADA in risk assessment. It was discovered that, for example, general ledger analysis, process mining, and other standardized data analytical tools are used by auditors in the planning phase of an audit. An improved overall understanding of the entity is formed as increased amount of data is processed, and the ADA applications guide auditors in finding the areas of financial statement including the most risk. Consequently, more precise and targeted audit measures are possible, and unnecessary substantive procedures are avoided. Additionally, the advancements in control and process identification were discovered. The effective data analytical assessment of controls and processes is possible for only certain systems, but the importance of effective controls for ADA usage is noted by the interviewed experts. The results agree to some degree with previous research but particularly findings regarding auditing standards contradict the previous research. The thesis contributes to previous research with new practical knowledge within the field.