Rennes School of Business Thesis: AI's Impact on Accounting's Future

This thesis, submitted to Rennes School of Business, investigates the profound impact of Artificial Intelligence (AI) on the future of the accounting profession. It explores the historical context of AI's integration, from early automation to advanced machine learning, and its potential to reshape accounting tasks. The research delves into how AI can enhance efficiency, improve fraud detection, and transform the roles of accountants. The thesis reviews existing literature on AI's applications in accounting, discussing the automation of repetitive tasks, the use of AI in data analysis and decision-making, and the implications for job roles within the profession. The methodology includes an analysis of how AI, specifically machine learning models like Artificial Neural Networks and Random Forests, can be used to predict and detect financial fraud. The study analyzes transaction data to identify fraudulent patterns, offering insights into how AI can provide real-time solutions and improve accuracy. The conclusion emphasizes the need for accountants to embrace AI, focusing on how AI will drive advancements in bookkeeping and the future direction of the profession.

A thesis
On
The Impact of Artificial Intelligence on Future of
Accounting Profession
Name of the Student
Name of the University

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Executive Summary
Computer systems and software of the present age are capable of creating and exhibiting
intelligence. Artificial intelligence in accounting profession has automated several accounting
tasks and helped accountants and auditors to perform better and play a more effectual role.
Numerous accounting researches have been done to improve the application of artificial
intelligence in accounting and auditing tasks. Artificial Intelligence has mostly been applied
successfully only to the more structured, programmable and repetitive tasks in which
gathering human expertise is not extremely difficult (Moudud-Ul-Huq, 2014). Frey and
Osborne’s (2013) predicted that the accounting profession will face extinction. However,
advanced study and researches claim that technologies cannot replace human intelligence.
According to (Greenman, 2017), Artificial Intelligence in the accounting world will not
replace accountants, it will simply change the focus. This paper aims to review and analyse
the research efforts and debates on the application of complex artificial intelligence in this
profession. Is artificially intelligent system capable of performing more complex and
important task such as materiality assessments, detect fraud, going-concern decisions,
analytical review procedures?

Table of Contents
Chapter 1: Introduction........................................................................................................9
1.1 Background of the Study............................................................................................9
1.2 Problem Statement...................................................................................................10
1.3 Research Questions..................................................................................................12
1.4 Aim and Objectives..................................................................................................12
1.5 Rationale of the Study..............................................................................................12
1.6 Key Terms................................................................................................................13
Chapter 2: Literature Review.............................................................................................14
2.1 Introduction..............................................................................................................14
2.2 Introduction to Accounting:.....................................................................................14
2.3 What is Artificial Intelligence?................................................................................15
2.4 Automation and Artificial Intelligence in Accounting:...........................................16
2.5 Impact of AI on the accounting industry..................................................................20
2.5.1 Accounting Tasks Which Machines Can Do....................................................21
2.5.2 AI Doesn’t Mean Job Losses............................................................................22
2.6 New AI and Digital Technologies in Accounting profession..................................23
2.6.1 AI as a Tool for Business Enablement and Productivity Enhancement............23
2.6.2 Robotics Process Automation...........................................................................23
2.6.3 Advanced Analytics..........................................................................................24
2.7 Why Accountants Must Embrace Machine Learning..............................................25
2.7.1 The Spectrum of Artificial Intelligence............................................................25
2.7.2 Accounting Already Experienced Machine Learning.......................................26
2.7.3 The Impact of Biased Data on Inductive Reasoning.........................................26
2.7.4 Machine Learning Implications for Auditors....................................................27
2.7.5 Machine Learning Implications for Management Accountants........................27
2.7.6 Openings in Enterprise Use of Machine Learning............................................28

2.7.7 Bookkeepers Must Embrace Machine Learning...............................................28
2.8 Conclusion................................................................................................................29
Chapter 3: Research Methodology.....................................................................................30
3.1 Introduction..............................................................................................................30
3.2 Research Design.......................................................................................................30
3.3 Data Collection for analysis.....................................................................................31
3.3.1 Dataset related to predicting Fraud in Financial Payment Services..................31
3.4 Data Analysis Model................................................................................................31
3.5 Data analysis tool.....................................................................................................33
3.6 Limitations...............................................................................................................33
Chapter 4: Data Analysis...................................................................................................34
4.1 Introduction..............................................................................................................34
4.2 How to predict and detect fraud with Machine Learning........................................34
4.2.1 Which types of transactions are fraudulent?.....................................................34
4.2.2 What determines whether the feature isFlaggedFraud gets set or not?............34
4.2.3 Looking at Account Types................................................................................35
4.2.4 Looking at Transaction Types...........................................................................36
4.2.5 Looking balances before and after the transaction............................................36
4.2.6 Artificial Neural Networks................................................................................36
4.2.7 Model 2: Random Forest...................................................................................37
4.3 Discussion................................................................................................................38
Chapter 5: Conclusion and Recommendations..................................................................42
5.1 Conclusion................................................................................................................42
5.2 Recommendations....................................................................................................42
5.2.1 AI will drive advancement in bookkeeping......................................................43
5.2.2 Bookkeeping assignments that machines can figure out how to do..................43
5.3 Future direction........................................................................................................43

5.3.1 The open door in AI..........................................................................................43
5.3.2 The cutoff points of AI......................................................................................44
5.3.3 Critical thinking, as once huge mob..................................................................44
Reference............................................................................................................................45
Appendix............................................................................................................................48

List of tables
Table 1: AI in accounting...................................................................................................19
Table 2: Automation in Accounting...................................................................................26

Table of figures
Figure 1: AI funding by accounting firms..........................................................................27
Figure 2: Transaction type to identify fraud......................................................................43
Figure 3: Fraudulent feature...............................................................................................44
Figure 4: Accounting type..................................................................................................45
Figure 5: Transaction Type................................................................................................45
Figure 6: ANN network.....................................................................................................47
Figure 7: Random Forest....................................................................................................48

DEB: Double-entry bookkeeping
AIS: Accounting Information System
AI: Artificial Intelligence
EY: Ernst & Young
PwC: Price Waterhouse and Coopers
KPMG: Klynveld Peat Marwick Goerdeler
IDP: Integrated Data Processing
AMP: AUDIT MASTERPLAN
ICOR: Integrated Capital and Operations Reporting
ES: Expert System
FSA: Financial Statement Analyzer
EDGAR: Electronic Data Gathering, Analysis and Retrieval
TDE: Transaction Data Enrichment
RPA: Robotic Process Automation
IA: Intelligent Automation