Artificiel Intelligence in a going concern assessment: En undersøgelse af hvordan Artificiel Intelligence kan understøtte revisors vurdering af going concern.
Student thesis: Master Thesis and HD Thesis
- Theis Lageri Strandberg
- Marcus Elster Damgaard
4. term, Business Economics and Auditing, Master (Master Programme)
It appears from ISA 570, that the auditor is responsible for obtaining sufficient and suitable audit evidence, to be able to conclude whether a company’s financial statements appropriately are prepared on a going concern basis. According to the going concern principle, in which this conclusion must be based on the auditor’s suitable and sufficient audit evidence. It appears from this thesis’s problem area, a sample survey prepared by FSR – Danske revisorer, conducted on a selected number of companies.
This sample survey showed that 4 out of 10 of the examined companies annual reports, neither had appropriate reservations nor supplementary information regarding going concern. This indicates a need for a tool to support the auditor’s assessment, of the company’s ability to continue operations. Which leads on to the purpose of this thesis.
The purpose of this thesis is to investigate how artificial intelligence can support the auditor’s assessment of going concern. Here the thesis will examine how machine learning can be used as a tool, to support the initial risk assessment of a company’s probability, of containing significant uncertainty regarding going concern. This thesis will be divided into three sub-questions to support the examination of this thesis.
The first sub-question was based on providing an understanding, of how going concern is assessed and reported in practice, as well as defining where in this process, the thesis’s AI system will be used, regarding the audit process. In addition, the expectations of state authorized and registered accountants for such a tool were investigated.
The thesis’s second sub-question aimed to uncover both which advantages and which disadvantages were expected to occur, when using an AI system, to support the risk assessment of going concern and in other auditing aspects.
The thesis’s third sub-question, aimed to illustrate how a technical example, of how an AI-system could assess a company’s probability, to contain uncertainty for going concern.
This thesis concluded that it was evident, that an AI-system could be beneficially used as a tool to support the quality of the initial going concern risk assessment, and thereby enhance the going concern assessment process by achieving an improved audit planning.
This sample survey showed that 4 out of 10 of the examined companies annual reports, neither had appropriate reservations nor supplementary information regarding going concern. This indicates a need for a tool to support the auditor’s assessment, of the company’s ability to continue operations. Which leads on to the purpose of this thesis.
The purpose of this thesis is to investigate how artificial intelligence can support the auditor’s assessment of going concern. Here the thesis will examine how machine learning can be used as a tool, to support the initial risk assessment of a company’s probability, of containing significant uncertainty regarding going concern. This thesis will be divided into three sub-questions to support the examination of this thesis.
The first sub-question was based on providing an understanding, of how going concern is assessed and reported in practice, as well as defining where in this process, the thesis’s AI system will be used, regarding the audit process. In addition, the expectations of state authorized and registered accountants for such a tool were investigated.
The thesis’s second sub-question aimed to uncover both which advantages and which disadvantages were expected to occur, when using an AI system, to support the risk assessment of going concern and in other auditing aspects.
The thesis’s third sub-question, aimed to illustrate how a technical example, of how an AI-system could assess a company’s probability, to contain uncertainty for going concern.
This thesis concluded that it was evident, that an AI-system could be beneficially used as a tool to support the quality of the initial going concern risk assessment, and thereby enhance the going concern assessment process by achieving an improved audit planning.
Language | Danish |
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Publication date | 1 Jun 2023 |
Number of pages | 79 |