The practical application of data analytics in audit risk assessment
Salminen, Markus (2025-03-18)
The practical application of data analytics in audit risk assessment
(18.03.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
suljettu
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025032521084
https://urn.fi/URN:NBN:fi-fe2025032521084
Tiivistelmä
This thesis examines the practical application of data analytics in audit risk assessment. The exponential growth of digitalization and data volume has significantly transformed the business environment, increasing the demands placed on auditing. Traditional audit methods are not always sufficient to meet the needs of modern business operations, and the use of audit data analytics provides opportunities for more precise analysis, enhanced risk assessment, and improved decision-making.
The study employs a qualitative research approach, collecting data through interviews with four experts from BIG4 auditing firm. The interviews provide in-depth insights into the use of data analytics in risk assessment, particularly in areas such as visualizing the revenue recognition process in percentage-of-completion accounting with advanced tools, identifying slow-moving inventory items, and conducting detailed analyses of purchases and personnel expenses. The findings indicate that data analytics enables the handling of extensive data sets, enhances the identification of risks, and supports more accurate conclusions. Furthermore, it improves audit coverage and quality compared to traditional sampling-based methods.
In addition to its benefits, the findings underscore the growing need for collaboration between auditors and data specialists. Data analytics tools often require specific expertise, and their integration into audit workflows necessitates clear communication and the ability to interpret complex datasets. The study highlights the importance of fostering cross-functional collaboration within audit teams to fully leverage the capabilities of audit data analytics. This approach not only enhances the accuracy of risk assessment but also promotes innovation in audit practices.
The study also identifies challenges related to the use of audit data analytics, such as the specialized skills required to operate analytical tools and the complexities involved in interpreting the results. Developing auditors’ expertise and updating international auditing standards are highlighted as critical prerequisites for the full-scale adoption of data analytics.
The thesis demonstrates that combining data analytics with traditional audit methods can significantly enhance the efficiency and reliability of audits. It offers practical perspectives on the application of audit data analytics and provides recommendations for future research.
The study employs a qualitative research approach, collecting data through interviews with four experts from BIG4 auditing firm. The interviews provide in-depth insights into the use of data analytics in risk assessment, particularly in areas such as visualizing the revenue recognition process in percentage-of-completion accounting with advanced tools, identifying slow-moving inventory items, and conducting detailed analyses of purchases and personnel expenses. The findings indicate that data analytics enables the handling of extensive data sets, enhances the identification of risks, and supports more accurate conclusions. Furthermore, it improves audit coverage and quality compared to traditional sampling-based methods.
In addition to its benefits, the findings underscore the growing need for collaboration between auditors and data specialists. Data analytics tools often require specific expertise, and their integration into audit workflows necessitates clear communication and the ability to interpret complex datasets. The study highlights the importance of fostering cross-functional collaboration within audit teams to fully leverage the capabilities of audit data analytics. This approach not only enhances the accuracy of risk assessment but also promotes innovation in audit practices.
The study also identifies challenges related to the use of audit data analytics, such as the specialized skills required to operate analytical tools and the complexities involved in interpreting the results. Developing auditors’ expertise and updating international auditing standards are highlighted as critical prerequisites for the full-scale adoption of data analytics.
The thesis demonstrates that combining data analytics with traditional audit methods can significantly enhance the efficiency and reliability of audits. It offers practical perspectives on the application of audit data analytics and provides recommendations for future research.