Dataanalytisk revision: En analyse af i hvilket omfang revision via dataanalyse kan erstatte den traditionelle tilgang til revision

Steven Due Dickow & Hanni Christiansen

Student thesis: Master thesis


The purpose of this thesis is to investigate in which way Data Analytics can replace the traditional way of auditing. It was analyzed what traditional way of auditing is like today, in which ways Data Analytics is conducted today, what it will require from the standards to faciliate Data Analytics in the future and finally a framework about how Data Analytics can be used in the future is given. To answer this question a number of 1-1 interviews has been conducted with key experts within the field of Data Analytics from accounting firms and other relevant institutations: FSR, IAASB, DAWG, and Erhvervsstyrelsen. A case company and its data set have been applied to exemplify and test the use of Data Analytics. In particular, much external material has been used, e.g. the standards, laws, existing teory and litteratur and a number of articles.
The key benefits of using Data Analytics are 1) via full test populations and larger amounts of transactions, it is much faster and more effective to identify risks and develop an response, 2) higher quality of the auding due to new insgihts about the compnay by not making the audit “though” the company” but “arround it”, and 3) fraud will be easier to indetify/detect by usage of tools and techologies.
It is broadly held that the standards, dispite lacking suggestions to use Data Analytics are not to be ssen as hindring its use. The ISA standards are only to be seen as an indirect barrier for the use of Data Analtyics due to lack of information. It is more the indivudal auditor`s mindset being a barrierer. The stadard setters and regulators are to communicate more clearly to handle this barrier among the uncertain auditors. In particular guidelines are seen as key in terms of spreading the use of Data Analtyics.
For the future use of Data Analtyics, a framwork is being suggested for the risk assesment stage. This framework highlights the chance to use Big Data, the different analytical approaches that can be taken, e.g. correlation analysis, and finally the important role of Data Visualisation. In particular the chance to use visualisation is seen as a key driver for making risk assesment more effective and efficient via Data Analytics.
It is concluded that Data Analytics is capable of replacing the traditional way of auditing during the whole auditing process in relation to the traditional auditing tasks and risk asssment. However, Data Analytics can never stand alone - it must be supported by trandtional auditing tasks (e.g. requests to management). Moreover, it is very important that use of Data Analytics is supprted by the auditor`s own proffesional judgemnet.

EducationsMSc in Auditing, (Graduate Programme) Final Thesis
Publication date2017
Number of pages213