Cover
Preface
Acknowledgments
PART I: Foundations for AI and Audit
1. CHAPTER 1: Introduction: Staying Ahead of the Emergent Risk
  1. KILLER ROBOTS AND AUDIT
  2. BUILDING THE AUDIT FUNCTION AROUND AI
  3. STAYING AHEAD OF THE EMERGENT RISK
  4. THE MUCH‐NEEDED EVOLUTION OF THE AUDIT FIRM
  5. THE CLAUSTROPHOBIC MINI UNIVERSE OF AUDIT
  6. THE PROBLEMS WITH TODAY'S APPROACH
  7. ENABLING NEW THINKING IN AUDIT
  8. REINVENTING THE LEGACY FIRM
  9. THE UNIVERSE OF ACCOUNTING
  10. THE GAME PLAN
  11. REFERENCES
2. CHAPTER 2: Fourth Industrial Revolution and Its Impact on Audit
  1. FOURTH INDUSTRIAL REVOLUTION
  2. HOW IS THE COGNITIVE REVOLUTION DIFFERENT FROM PREVIOUS REVOLUTIONS?
  3. YOU AND THE NEW ERA
  4. OPPORTUNITIES AND PERILS OF THE FOURTH INDUSTRIAL REVOLUTION
  5. SOURCES OF COMPETITIVE ADVANTAGE IN THE FOURTH REVOLUTION
  6. AUDIT MAGNIFICATION
  7. AUTOMATION = 3 AS (AUDIT, ACTIVISM, ASSURANCE)
  8. REFERENCES
3. CHAPTER 3: What Is Artificial Intelligence?
  1. A BRIEF HISTORY OF AI
  2. LAUNCHPAD FACTORS
  3. DATA SCIENCE, MACHINE LEARNING, AI, AND EVERYTHING ELSE
  4. DEFINITION OF AI
  5. TURING TEST
  6. NARROW VERSUS GENERAL AI
  7. VIEW OF THE ENTERPRISE
  8. TOOLKIT FOR INTELLIGENT AUDIT AUTOMATION
  9. REFERENCES
4. CHAPTER 4: Rise of Machine Learning
  1. POWER OF PATTERNS
  2. WHAT IS MACHINE LEARNING?
  3. MACHINE LEARNING IS REALLY SCIENTIFIC PROCESS ON STEROIDS
  4. THE LEARNING MACHINE
  5. ALGORITHMS
  6. ACCUMULATING EXPERIENCE
  7. TECHNIQUES OF LEARNING
  8. SUPERVISED LEARNING
  9. UNSUPERVISED LEARNING
  10. REINFORCEMENT LEARNING
5. CHAPTER 5: Machine Learning
  1. MODELS
  2. FEATURES AND TARGETS
  3. MODEL INDUCTION
  4. DATA
  5. STOP AND THINK!
  6. SUPERVISED: CLASSIFICATION WITH TREES
  7. CLASSIFICATION USING MATHEMATICAL FUNCTIONS
  8. SUPERVISED: SIMPLE LINEAR CLASSIFIER
  9. SUPERVISED: SUPPORT VECTOR MACHINE
  10. SUPERVISED: NAïVE BAYES
  11. SUPERVISED: BAYESIAN BELIEF NETWORKS
  12. SUPERVISED: K‐NEAREST NEIGHBOR
  13. SUPERVISED: REGRESSION
  14. SUPERVISED: MULTIDIMENSIONAL REGRESSION
  15. UNSUPERVISED
  16. NEURAL NETWORKS
  17. PREPROCESSING THE DATA
  18. REFERENCES
  19. NOTE
6. CHAPTER 6: Building an IAA Audit Firm
  1. WHY PLAN AND DESIGN?
  2. STUDYING AGENTS
  3. CONCLUSION TO PART ONE
  4. REFERENCES
PART II: Building the Automated Audit Function in the Enterprise
1. CHAPTER 7: Obtain, Retain, and Preplan with AI
  1. SOLVING THE BEHAVIORAL PROBLEMS IN AUDIT
  2. AUTOMATED PREPLANNING SUBPROCESSES
  3. AUTOMATED PRE‐ENGAGEMENT
  4. AUTOMATED AUDIT PLANNING
  5. AUTOMATED PRE‐ENGAGEMENT ACTIVITIES AND WORK TASKS
  6. PRE‐ENGAGEMENTSADAL
  7. EXAMPLES OF MACHINE LEARNING TO IDENTIFY DECEPTION
  8. AUTOMATED AUDIT PLANNING ACTIVITIES AND WORK TASKS
  9. AUTOMATED AUDIT PLANNINGSADALS
  10. CLIENT BUSINESS STRATEGY AGENTS
  11. REFERENCES
2. CHAPTER 8: Automated Inherent Risk Assessment
  1. TOTAL AUDIT RISK
  2. INHERENT RISK
  3. UNDERSTANDING THE BUSINESS PROBLEM
  4. AUDITOR'S THOUGHT PROCESS
  5. GENERAL APPROACH FOR AUTOMATION
  6. UNDERSTANDING THE FEATURES USED IN VARIOUS APPROACHES
  7. ACCOUNT RISK
  8. REFERENCES
3. CHAPTER 9: Automating Internal Controls Assessment
  1. AUTOMATING INTERNAL CONTROLS ASSESSMENT
  2. AUTOMATED CONTROL ENVIRONMENT
  3. AUTOMATED RISK ASSESSMENT
  4. CONTROL ACTIVITIES
  5. AUTOMATED MONITORING
  6. INFORMATION AND COMMUNICATIONS
  7. CONTROL RISK AND NEXT STEPS
  8. REFERENCES
4. CHAPTER 10: Automated Procedures
  1. ACCUMULATING EVIDENCE
  2. BEYOND INTERNAL CONTROLS
  3. AUTOMATING BASIC FUNCTIONS
  4. AUTOMATED RECONCILIATION
  5. AUTOMATED JOURNAL ENTRIES
  6. PUTTING IT ALL TOGETHER
  7. REFERENCES
5. CHAPTER 11: Reporting and Post‐Audit Management
  1. AUDIT OPINION DETECTION
  2. POST‐AUDIT MANAGEMENT
  3. CHALLENGES IN ACHIEVING THE MATURITY
  4. BUILDING AN IN‐HOUSE ARCHITECTURE FOR AN AUDIT FIRM (OR INTERNAL AUDIT)
  5. A FEW IMPORTANT THINGS TO CONSIDER
  6. REFERENCES
PART III: Forensic Accounting Automation
1. CHAPTER 12: Intelligent Automation of Fraud Detection
  1. DETECTING FRAUD
  2. ELEMENTS OF FRAUD
  3. DOMAIN‐SPECIFIC FRAUD DETECTION
  4. STOPSCAM
  5. TECHNOLOGIES AND MODELS
  6. OUR APPROACH
  7. REFERENCES
2. CHAPTER 13: Forensic Accounting
  1. FORENSIC ACCOUNTING
  2. CAPABILITY BUILDING IN FORENSIC ACCOUNTING
  3. REFERENCES
3. CHAPTER 14: Managing for Value and Valuation
  1. INFINITY CYCLE
  2. INFINITY CYCLE AND FINANCIAL FRAUD
  3. VALUATION
  4. REFERENCES
4. CHAPTER 15: Tying It Together and Robots
  1. CYCLES OF AUDIT
  2. ROBOTS: A REVIEW
  3. REFERENCES
PART IV: Management, Organization, and Governance for AI in Audit
1. CHAPTER 16: Client Management
  1. 3P MODEL
  2. MACHINE LEARNING IN SALES
  3. REFERENCES
2. CHAPTER 17: AI Organization and Project Management
  1. NEW CAPABILITY ASSESSMENT
  2. ISSUES OF THE NEW ORGANIZATION
  3. MANAGING AI PROJECTS
3. CHAPTER 18: Governance and Ethics
  1. SOME OBVIOUS ETHICAL ISSUES
  2. GOVERNANCE OF AI: AN INTRODUCTION TO THE PROBLEM
  3. HUMANS AND AI
  4. AIAI GOVERNANCE MODEL
  5. GOVERNANCE FRAMEWORK
  6. ETHICS CHARTER
  7. REFERENCE
Index
End User License Agreement

List of Illustrations

Chapter 1
1. FIGURE 1.1 Bidirectional Impact
2. FIGURE 1.2 The Wheel of Modern Finance
3. FIGURE 1.3 Intelligent Audit Automation Framework of AIAI
Chapter 2
1. FIGURE 2.1 The New Professional for the AI Era
2. FIGURE 2.2 A Different Type of Machine
3. FIGURE 2.3 The Workspace Automation
Chapter 3
1. FIGURE 3.1 Turing Test
2. FIGURE 3.2 Enterprise Automation History
Chapter 4
1. FIGURE 4.1 Data, Features, and Target
2. FIGURE 4.2 The Process of Using Examples in Supervised Learning
Chapter 5
1. FIGURE 5.1 Identifying a Car
2. FIGURE 5.2 Data, Features, and Target
3. FIGURE 5.3 Function-Based Split
4. FIGURE 5.4 Predicting Vote
5. FIGURE 5.5 Solution Space, Many Lines Possible
6. FIGURE 5.6 Support Vector Machine
7. FIGURE 5.7 Non‐Separable
8. FIGURE 5.8 Inseparable
9. FIGURE 5.9 Inseparable Separated via Transformation
10. FIGURE 5.10 k‐Nearest Neighbor
11. FIGURE 5.11 Finding the Function
12. FIGURE 5.12 Plot
13. FIGURE 5.13 Random Placement of a Line
14. FIGURE 5.14 Adding Lines
15. FIGURE 5.15 Finding the Best Fit Line
16. FIGURE 5.16 Step 1 of Clustering k‐Means
17. FIGURE 5.17 Step 2 k‐Means Clustering
18. FIGURE 5.18 Step 3 k‐Means Clustering
19. FIGURE 5.19 Step 4 k‐Means Clustering
20. FIGURE 5.20 Predicting with a Function
21. FIGURE 5.21 Using a Neural Network to Predict
22. FIGURE 5.22 How a Neuron Passes the Output to the Next Layer
23. FIGURE 5.23 A Neural Network
24. FIGURE 5.24 Deep Learning
Chapter 6
1. FIGURE 6.1 Extracting DTC Tasks for Automation
2. FIGURE 6.2 DTC Model
3. FIGURE 6.3 The SADAL^® Process
4. FIGURE 6.4 The Journey Forward
Chapter 7
1. FIGURE 7.1 Automated Preaudit Evaluation
2. FIGURE 7.2 Pre‐Engagement Activities
3. FIGURE 7.3 Audit Planning
4. FIGURE 7.4 Value Network
Chapter 8
1. FIGURE 8.1 Approaches to Automated Inherent Risk Assessment
2. FIGURE 8.2 The Risk Drivers in Inherent Risk
3. FIGURE 8.3 Accounts Receivable
Chapter 9
1. FIGURE 9.1 Automation of Internal Controls Evaluation
2. FIGURE 9.2 Automated Environment Evaluation
Chapter 10
1. FIGURE 10.1 The Audit Automation Map
2. FIGURE 10.2 Audit Procedures and Testing
3. FIGURE 10.3 The Accounting Process
Chapter 11
1. FIGURE 11.1 The Audit Automation
2. FIGURE 11.2 Data Classes
3. FIGURE 11.3 Models and Maturity
4. FIGURE 11.4 The Four Layers for the AI Era
Chapter 12
1. FIGURE 12.1 The Fraud Tree
Chapter 14
1. FIGURE 14.1 The Infinity Cycle
2. FIGURE 14.2 Value Destruction
3. FIGURE 14.3 Fraud in the Value Creation Cycle
4. FIGURE 14.4 Shareholder Value Creation
Chapter 15
1. FIGURE 15.1 The Cycles of Audit
2. FIGURE 15.2 The PRMIT Model
3. FIGURE 15.3 Revenue Cycle Processes
Chapter 17
1. FIGURE 17.1 AI Organizational Planning
Chapter 18
1. FIGURE 18.1 The AIAI Governance Model
2. FIGURE 18.2 The AIAI Governance Framework

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging‐in‐Publication Data

Names: Naqvi, Ali S., 1968‐ author.

Title: Artificial intelligence for audit, forensic accounting, and valuation : a strategic perspective / Ali S. Naqvi.

Description: First edition. | Hoboken : Wiley, 2020. | Includes index.

Identifiers: LCCN 2020021960 (print) | LCCN 2020021961 (ebook) | ISBN 9781119601883 (cloth) | ISBN 9781119601913 (adobe pdf) | ISBN 9781119601937 (epub)

Subjects: LCSH: Auditing–Data processing. | Forensic accounting. | Valuation. | Artificial intelligence.

Classification: LCC HF5667.12 .N37 2020 (print) | LCC HF5667.12 (ebook) | DDC 658.0285/63–dc23

LC record available at https://lccn.loc.gov/2020021960

LC ebook record available at https://lccn.loc.gov/2020021961

Cover Design: Wiley

Cover Image: Dragon © Nipitpon Singad/EyeEm/Getty Images

Preface

AUDITORS AND ASSURANCE PROFESSIONALS: Do you know how to build a comprehensive plan to achieve intelligent automation of your audit function? More importantly, at personal and professional levels, are you ready for the greatest transformation in the human history?

An artificial intelligence revolution is sweeping through the world. Auditors and forensic accountants in firms of all sizes and types are trying to understand what it means for them. Many want to explore how to build plans for total intelligent automation to move their companies forward into the new AI economy. Many have recognized the imminent need for augmenting skills because of increasing automation. The recognition that the AI economy will be led by business professionals is finally sinking in. But auditors, accountants, finance professionals, and forensic accountants must develop a deeper and more pragmatic understanding of the AI revolution. They must do it fast before the opportunity passes them by. This book will be your guide to improve and implement intelligent automation in your firms – and to do it in a structured, efficient, and disciplined way.

Artificial intelligence and audit are not strangers to each other. For decades researchers and practitioners have been trying to marry the two. Audit, if automated, can improve the audit quality and reduce its cost. On one hand it will improve the audit efficiency for companies; on the other hand it will enhance the trust and confidence of investors and other stakeholders who rely upon audited financial statements.

While the research and reports on artificial intelligence–centric audit automation have been plenty, there has not been a structured model of end‐to‐end audit automation. Sporadic collection of papers on audit does not translate into a workable audit model. Neither does haphazard capability building. In some ways, it was never deemed important to build a comprehensive framework from the perspective of how to build an audit automation plan for a company. After all, up until very recent times, the technology was not advanced enough to even begin to think about a fully automated audit. It was no different than the application of artificial intelligence in a car or an airplane. Automating some functional parts of a car's or an airplane's operation is one thing, creating an autonomous car or an autonomous drone another. But just as advances in technology have now given us the comfort to envision putting autonomous vehicles on the road and autonomous drones in the sky, the time for autonomous and continuous audit has come.

However, automating audit does not mean eliminating human work. If anything, it means eliminating the mistakes and errors, whether intentional or not, of the human work. Thus, a structured model of intelligent automation of audit will incorporate the automation of both cognitive and physical human work and will focus on enhancing the human ability to function far beyond what normal human capacities allow humans to do. Lifting the human constraints is not simply a function of enhanced computational efficiency. It results from embedding intelligence in machines.

Embedding intelligence in machines is no ordinary change. It is one of the most extraordinary developments in the course of human history. Machines have always been subservient to human control. The new type of machine, with a mind of its own, is something humans have no experience with. This new development implies that we cannot approach intelligent automation in audit as a standalone phenomenon. We must consider the management, economic, technical, organizational, and governance aspects of introducing this magnificent technology. The book addresses all of those areas and provides a comprehensive first look at the entire discipline of intelligent automation in audit.

The book has been divided into four parts. Part One orients you to the fascinating world of audit automation. By addressing the following questions, it establishes the foundational knowledge for an AI business professional:

What is the AI revolution and how does it impact economy?
What is the role of an audit professional? How does AI change that role?
What is artificial intelligence technology?
What are the best practices and methodologies to build intelligent solutions?

In the first six chapters, this book walks you through the amazing forces that have come together to launch the AI revolution. Chapter 5 introduces machine learning and explains some of the algorithms. The book is written for business audience and therefore the idea is not to make you a machine learning expert but instead to equip you with the knowledge necessary for you to take ownership of business solutions in AI.

With the foundational knowledge covered in Part One, we then introduce the intelligent automation of audit model in Part Two. This is a structured model that gives companies the ability to envision and understand what a comprehensive automated audit solution looks like. We dedicate five chapters (7 to 11) to the automation of the audit process. Each of the five chapters addresses a process area of audit, including preplanning, inherent risk, controls risk, audit procedure, and post‐audit management. The approach in this book is to focus on automation. Since the book is authored for a global audience, reference to accounting standards of any single country was intentionally avoided.

After covering the audit process automation, in Part Three the focus shifts to fraud detection, forensic accounting, and valuation. Part Three is designed to equip assurance firms to develop automation to offer forensic services. The automation strategy in those areas is defined by introducing a model known as Infinity Cycle and building automation around that model. In four chapters (12 to 15) we cover various aspects of traditional and modern forensic accounting to build a highly automated forensic automation firm.

This book is about practical solutions. In the first three parts we covered the technical and automation aspects of intelligent automation of audit. Those parts are incomplete if we fail to address the fact the intelligent automation is not just changing the technology, it requires changing our approach and understanding of the organization. The organization in the AI economy will look and feel different than the one in the digital economy. The AI solution development will also require a change in how projects are developed. An intelligent solution also requires different types of governance than a non‐intelligent machine. Developing a pragmatic solution necessitates addressing organizational issues, project management, and governance. Part Four has three chapters (16 to 18) and they address those critical solution development issues.

How to get the most out of this book?

There are three ways to get the most out of this book. First, read it with the mindset that you are designing and envisioning the future of your firm and the audit/assurance function. This means to think in terms of the framework. Second, as a businessperson, unless you want to, you don't have to understand the technical details beyond what is presented in this book. To take ownership of projects, you need enough knowledge to have a reasonable conversation with your data science or AI team. This book enables you to do that. If you encounter a technical concept that you don't grasp, try to understand it with the analogies provided in the book. Third, apply the knowledge by building a working plan for your audit automation.

Given the state of the technology, intelligent automation of audit is imminent. Start early to get a strong lead. Stay ahead by doing it effectively. Your journey has just begun. Expect the world to be transformed in the next few years. Have a wonderful journey!

Artificial Intelligence for Audit, Forensic Accounting, and Valuation

A Strategic Perspective

Preface

Acknowledgments

PART I
Foundations for AI and Audit