Artificial Intelligence in Accounting

Introduction    9

Chapter 1. Understanding Artificial Intelligence — Definitions, History, and Tools    13
1.1.    Artificial Intelligence — Definitions, Boundaries, and Meanings     13
1.2.    The History of Chatbots — Conversations That Changed the World    15
1.2.1.    ELIZA — The Ancestor    15
1.2.2.    PARRY — The Vision of Chatbots’ Potential    16
1.2.3.    Jaberwacky — Addressing the Limitations    17
1.2.4.    Siri — The Impact of Chatbots    18
1.3.    The History of Artificial Intelligence — From Dreams to First Successes    20
1.3.1.    Humanity’s Early Dream    20
1.3.2.    The Dawn of the Age of AI    20
1.3.3.    Perceptron — The First Spark of Machine Intelligence    22
1.3.4.    AI Winters — Roadblocks in Development    22
1.3.5.    The Resurgence of AI — Breakthroughs in Backpropagation    23
1.4.    The History of Artificial Intelligence — Breakthroughs, Crises, and New Hopes    24
1.4.1.    New Models, New Potential — Bayesian Networks and LSTM    24
1.4.2.    Deep Blue — The Machine That Beat the World Champion    25
1.4.3.    The Deep Learning Revolution — Hinton’s Legacy    26
1.4.4.    GPUs — Fuelling the Data-Hungry Brain    26
1.4.5.    GANs — Machines that Imagine    27
1.4.6.    GPT’s — Giving Machines a Voice    27
1.4.7.    AlphaFold 2 — AI Meets Biology    28
1.4.8.    From DALL·E and Sora — AI in Art    29
1.4.9.    The Oracle — ChatGPT and Its Impact    29
1.5.    The AI Ecosystem — Introduction and Its Practical Relevance    30
1.5.1.    A First Look at Today’s AI Ecosystem    30
1.5.2.    ChatGPT (Platform by OpenAI)    31
1.5.3.    Claude (Platform by Anthropic)    32
1.5.4.    Gemini (Platform by Google DeepMind)    32
1.5.5.    Copilot (Platform by Microsoft)    33
1.5.6.    Perplexity (Platform by Perplexity AI)    33
1.5.7.    Grok (Platform by xAI)    34
1.5.8.    DeepSeek (Platform by DeepSeek AI)    34
1.5.9.    LLaMA-Based Tools (Various Platforms)    35

Chapter 2. The History of Technology and Artificial Intelligence in Accounting — From Invention to Automation    37
2.1.    The History of Invention — From Fire to Algorithm as the Context for AI Development    37
2.1.1.    The Technological March of Humanity Toward Intelligence    37
2.1.2.    Inventions as a Mirror of Social and Economic Changes    39
2.1.3.    Four Industrial Revolutions: From the Steam Engine to Algorithms    40
2.1.4.    Toward Intelligent Systems — The Birth of Thinking Machines    41
2.2.    The Origins of Counting — From Myths and the Abacus to Early Computational Practices    42
2.2.1.    The Abacus — The First “Digital” Accounting Tool    42
2.2.2.    From Myth to Logic — Ancient Sources of Accounting Technologies    43
2.3.    The Age of Mechanical Dreams — Inventions That Transformed Accounting    46
2.3.1.    Leibniz’s Calculating Machine — The Mechanical Dream of Intelligence    46
2.3.2.    Mechanization and Digitization — From the Burroughs Machine to Mainframes    47
2.4.    The Dawn of the Digital Era — Microcomputers and Integrated Accounting Systems    51
2.4.1.    The Microcomputer Revolution and the Birth of Accounting Software    51
2.4.2.    From ERP to RPA — On the Integration and Automation of Accounting Systems    53
2.5.    Toward Intelligence — Machine Learning and Artificial Intelligence in 21st-Century Accounting    56
2.5.1.    The New Face of the Accounting Professional in the Digital Era    56
2.5.2.    Machine Learning and AI in 21st-Century Accounting    58

Chapter 3. Contemporary Applications of AI in Accounting — Directions and Processes    61
3.1.    Strategic and Managerial Directions of AI Applications     61
3.1.1.    The Strategic Importance of Applying AI in Accounting    61
3.1.2.    Performance Under Control — Modern Controlling Supported by AI    63
3.1.3.    Regulatory Intelligence — AI in Tax and Compliance    66
3.2.    Automation and Optimization of Accounting Processes    68
3.2.1.    Routine Automation — AI and RPA in Accounting Records    68
3.2.2.    Reporting 2.0 — Algorithms in Financial Reporting    71
3.3.    Analytical Support and Human–Machine Collaboration    73
3.3.1.    Prediction and Analytics — Forecasting with AI     73
3.3.2.    Detecting the Undetectable — Fraud and Anomaly Detection Using AI    76
3.3.3.    Human–Machine Collaboration — Cognitive Interfaces in Accountants’ Work    80

Chapter 4. AI in Practice — Case Studies from Leading Organizations Worldwide    85
4.1.    An Introduction to Inspiring Stories That Shape the Future of Accounting    85
4.2.    Strategic and Managerial Applications of AI    86
4.2.1.    Unilever — AI in Cost Optimization, Management Transformation and ESG Goals    86
4.2.2.    KPMG — AI in Financial Reporting and Auditing    87
4.2.3.    Ernst & Young — AI in Audit and Fraud Detection    89
4.3.    Process Automation and Operational Optimization    91
4.3.1.    Uber — Automation of Financial Processes    91
4.3.2.    Cognizant — The Use of RPA in the Mass Processing of Invoices    93
4.3.3.    IBM — AI and OCR in Invoicing    94
4.3.4.    Aceable — OCR in Cost Invoices    97
4.4.    Analytical Support and Human–Machine Collaboration    98
4.4.1.    Mastercard — Fraud Detection with GenAI    98
4.4.2.    Deloitte — DARTbot for Auditors    100
4.4.3.    Bank of America — Chatbot Erica    101

Key Findings and Summary    105

References    111