L'intelligence artificielle : quels impacts sur l'audit interne ?
Guyot, Marine
Promoteur(s) : Van Caillie, Didier
Date de soutenance : 3-sep-2019/10-sep-2019 • URL permanente : http://hdl.handle.net/2268.2/7870
Détails
Titre : | L'intelligence artificielle : quels impacts sur l'audit interne ? |
Titre traduit : | [en] Artificial Intelligence : Which impact on internal audit? |
Auteur : | Guyot, Marine |
Date de soutenance : | 3-sep-2019/10-sep-2019 |
Promoteur(s) : | Van Caillie, Didier |
Membre(s) du jury : | Ittoo, Ashwin
Colson, Christophe |
Langue : | Français |
Nombre de pages : | 154 |
Mots-clés : | [en] Internal Audit [en] Artificial Intelligence [en] SWOT Aanalysis [en] qualitative analysis [en] empirical research |
Discipline(s) : | Sciences économiques & de gestion > Comptabilité & audit |
Public cible : | Professionnels du domaine Etudiants |
Institution(s) : | Université de Liège, Liège, Belgique |
Diplôme : | Master en sciences de gestion, à finalité spécialisée en Financial Analysis and Audit |
Faculté : | Mémoires de la HEC-Ecole de gestion de l'Université de Liège |
Résumé
[en] Nowadays, the amount of data and calculation capabilities of algorithms are constantly increasing. It has become crucial for companies to manage them to improve their performance management and decision-making process in order to stay competitive. Therefore, having an effective steering tool to face this complex environment has become a major challenge. In this view, it is now critical for the profession of auditors to get ready to reshape their role, develop new skills and knowledge to meet new expectations.
In such conditions a question has raised: “Artificial Intelligence: which impact on internal audit?”
In this report, the topics covered concern the use of AI in the internal audit sector, implementation improvement’s areas as well as the vision of internal audit consultants towards it.
The applied methodology in order to answer our questions has been divided into three parts. Firstly, we have developed a literature review based on a deep analysis of the different characteristics of internal audit and AI.
Secondly, we have undertaken an empirical research to get a better understanding and a deeper knowledge of issues.
This qualitative analysis helped us finally to validate key success factors from both theoretical analysis and interviews and to spot the potential challenges professionals will have to overcome in the next years.
A summary study of it, has been developed through a SWOT analysis which confirms that AI systems go way beyond technical aspects and have to be considered globally to make his strategy successful.
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