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E-raamat: Machine Learning for Auditors: Automating Fraud Investigations Through Artificial Intelligence

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 26-Feb-2022
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484280515
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Machine Learning for Auditors: Automating Fraud Investigations Through Artificial IntelligenceSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 26-Feb-2022
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484280515
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Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings.

Machine Learning for Auditors provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization.


What You Will Learn

  • Understand the role of auditors as trusted advisors
  • Perform exploratory data analysis to gain a deeper understanding of your organization
  • Build machine learning predictive models that detect fraudulent vendor payments and expenses
  • Integrate data analytics with existing and new technologies
  • Leverage storytelling to communicate and validate your findings effectively
  • Apply practical implementation use cases within your organization


Who This Book Is For

AI Auditing is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes. 


Intermediate user level
About the Author xiii
About the Technical Reviewer xv
Introduction xvii
Part I Trusted Advisors
1(54)
Chapter 1 Three Lines of Defense
3(10)
Al, ML, and Auditing
3(2)
The Three Lines of Defense Model
5(2)
Risk Management Complexities
7(2)
The Three Lines Model
9(2)
Conclusion
11(2)
Chapter 2 Common Audit Challenges
13(20)
Data Literacy
14(2)
Manual Testing
16(3)
Data Sources
19(2)
Structured vs. Unstructured Data
21(2)
Citizen Developers
23(5)
Data Wrangling
28(1)
Data Bias
29(2)
Conclusion
31(2)
Chapter 3 Existing Solutions
33(10)
Substantive Testing
33(1)
CAATs
34(1)
Fit-for-Purpose Technologies
35(3)
Process Mining
38(3)
Continuous Auditing
41(1)
Conclusion
42(1)
Chapter 4 Data Analytics
43(6)
CRISP-DM
44(2)
Data Analytics Audit Applications
46(1)
Data Analytics vs. Data Science
47(1)
Conclusion
48(1)
Chapter 5 Analytics Structure and Environment
49(6)
Analytics Organization Structure
50(1)
Organization Climate
51(1)
The Role of Senior Leaders
52(1)
Conclusion
53(2)
Part II Understanding Artificial Intelligence
55(82)
Chapter 6 Introduction to AI, Data Science, and Machine Learning
57(16)
A Self-Driving Car
57(3)
Components of an AI System
60(3)
CRISP-DM for Data Science
63(4)
Domain Knowledge
67(1)
Payment Fraud/Anomaly Detection
68(4)
Conclusion
72(1)
Chapter 7 Myths and Misconceptions
73(4)
Myth #1 You Need an Advanced Degree to Be a Data Scientist
74(1)
Myth #2 Correlation Implies Causation
74(2)
Myth #3 The Model Building Is the Most Critical Step
76(1)
Conclusion
76(1)
Chapter 8 Trust, but Verify
77(12)
What Is Trust, but Verify?
77(3)
Why Is It Important to Verify?
80(3)
Integrated Reporting
83(4)
Conclusion
87(2)
Chapter 9 Machine Learning Fundamentals
89(28)
Supervised Learning
89(15)
Classifiers
90(1)
Decision Trees
91(2)
Random Forests
93(1)
Support Vector Machines
94(1)
Logistic Regression
95(1)
Naive Bayes
96(1)
Deep Learning
97(2)
Confusion Matrix
99(1)
ROC Curves
100(3)
Regression
103(1)
Linear Regression
103(1)
Unsupervised Learning
104(8)
Clustering Algorithms
105(1)
K-means Clustering
105(1)
Hierarchical Clustering
106(2)
Silhouette Score
108(1)
Elbow Plot
108(1)
Dimensionality Reduction
109(1)
Curse of Dimensionality
110(1)
Principal Component Analysis
110(1)
Scree Plots
111(1)
Overfitting, Underfitting, and Feature Extraction
112(4)
Overfitting
113(1)
Underfitting
114(1)
Feature Extraction
115(1)
Ensemble
116(1)
Conclusion
116(1)
Chapter 10 Data Lakes
117(6)
Introduction to Data Lakes
117(2)
Tangible Value
119(1)
Role as Analytics Enabler
119(1)
Architectures
120(2)
Conclusion
122(1)
Chapter 11 Leveraging the Cloud
123(8)
Local Workstation
123(2)
Cloud Computing
125(1)
Amazon SageMaker
126(1)
Google Colab
127(2)
IBM Watson
129(1)
Conclusion
130(1)
Chapter 12 SCADA and Operational Technology
131(6)
Fourth Industrial Revolution
131(2)
SCADA Auditing
133(1)
Applying AI to SCADA Auditing
134(1)
Conclusion
135(2)
Part III Storytelling
137(48)
Chapter 13 What Is Storytelling?
139(8)
Data Storytelling
139(3)
Common Pitfalls
142(2)
Misleading Graphs
142(1)
Anscombe's Quartet
143(1)
Engaging the Audience
144(1)
Conclusion
145(2)
Chapter 14 Why Storytelling?
147(4)
Why Does It Work?
147(1)
General Guidelines of Good Storytelling
148(1)
General Dashboard Layout
148(2)
Conclusion
150(1)
Chapter 15 When to Use Storytelling?
151(4)
Use Stories to Inspire or Motivate an Action
151(1)
When Can We Use Storytelling?
152(1)
Less Is More
153(1)
Conclusion
154(1)
Chapter 16 Types of Visualizations
155(14)
Basic Visuals
155(8)
Advanced Visuals
163(4)
One-Hot Encoding
167(1)
Conclusion
167(2)
Chapter 17 Effective Stories
169(4)
Case Study: "The Best Stats You've Ever Seen"
169(1)
Case Study: "U.S. GUN KILLINGS IN 2018"
170(1)
Case Study: "Numbers of Different Magnitudes"
171(1)
Recap of Effective Storytelling Elements
172(1)
Conclusion
172(1)
Chapter 18 Storytelling Tools
173(8)
Technical Expertise
173(1)
Available Tools
174(5)
Qlik
174(1)
Power BI
175(1)
Tableau
175(1)
Mode Analytics
176(3)
Conclusion
179(2)
Chapter 19 Storytelling in Auditing
181(4)
Audit Use Cases
181(1)
Communication of Findings
181(1)
Support Recommendations
182(1)
Clarify Business Knowledge
183(1)
Conclusion
183(2)
Part IV Implementation Recipes
185(52)
Chapter 20 How to Use the Recipes
187(6)
What Is a Recipe?
187(1)
Prerequisites
188(1)
Where Can You Find the Python Code?
188(1)
Implementation Considerations
189(2)
Conclusion
191(2)
Chapter 21 Fraud and Anomaly Detection
193(10)
The Dish: A Fraud and Anomaly Detection System
193(1)
Ingredients
194(2)
Instructions
196(5)
Step 1 Data Preparation
196(1)
Step 2 Exploratory Data Analysis
196(2)
Step 3 Apply Interquartile Range (IQR) Method
198(1)
Step 4 Perform Supervised Learning
199(1)
Step 5 Perform Unsupervised Learning Analysis
199(1)
Step 6 Review Exceptions with Additional Data
200(1)
Step 7 Re-evaluate the Models
201(1)
Variation and Serving
201(2)
Chapter 22 Access Management
203(6)
The Dish: ERP Access Management Audit
203(1)
Ingredients
204(1)
Instructions
204(3)
Step 1 Data Preparation
204(1)
Step 2 Exploratory Data Analysis
205(1)
Step 3 Scatter Plot of ERP Access
205(1)
Step 4 Review Exceptions with Additional Data
206(1)
Step 5 Reperform the Analysis
207(1)
Variation and Serving
207(2)
Chapter 23 Project Management
209(6)
The Dish: Project Portfolio Analysis
209(1)
Ingredients
210(1)
Instructions
210(4)
Step 1 Data Preparation
211(1)
Step 2 Exploratory Data Analysis
211(1)
Step 3 Perform Random Forest Classification
211(2)
Step 4 Review Feature Importance
213(1)
Variation and Serving
214(1)
Conclusion
214(1)
Chapter 24 Data Exploration
215(4)
The Dish: Understanding the Data Through Exploration
215(1)
Ingredients
215(1)
Instructions
216(2)
Step 1 Data Preparation
216(1)
Step 2 Exploratory Data Analysis
216(2)
Variation and Serving
218(1)
Conclusion
218(1)
Chapter 25 Vendor Duplicate Payments
219(4)
The Dish: Vendor Duplicate Payments Analysis
219(1)
Ingredients
220(1)
Instructions
220(1)
Step 1 Data Preparation
220(1)
Step 2 Perform K-NN Algorithm
220(1)
Step 3 Review Exceptions with Additional Data
220(1)
Variation and Serving
221(1)
Conclusion
221(2)
Chapter 26 Caats 2.0
223(6)
The Dish: CAATs Analysis Using ML
223(1)
Ingredients
224(1)
Instructions
224(2)
Step 1 Data Preparation
225(1)
Step 2 Exploratory Data Analysis
225(1)
Step 3 K-Means Clustering
225(1)
Variation and Serving
226(1)
Conclusion
227(2)
Chapter 27 Log Analysis
229(6)
The Dish: NLP Log Analysis
229(1)
Ingredients
230(1)
Instructions
230(3)
Step 1 Data Preparation
230(1)
Step 2 Exploratory Data Analysis
231(1)
Step 3 Perform Topic Modeling
232(1)
Step 4 Reperform the Analysis
233(1)
Variation and Serving
233(1)
Conclusion
233(2)
Chapter 28 Concluding Remarks
235(2)
Index 237
Maris Sekar is a professional computer engineer, Certified Information Systems Auditor (ISACA), and Senior Data Scientist (Data Science Council of America). He has a passion for using storytelling to communicate on high-risk items within an organization to enable better decision making and drive operational efficiencies. He has cross-functional work experience in various domains such as risk management, data analysis and strategy, and has functioned as a subject matter expert in organizations such as PricewaterhouseCoopers LLP, Shell Canada Ltd., and TC Energy. Maris love for data has motivated him to win awards, write LinkedIn articles, and publish two papers with IEEE on applied machine learning and data science.
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