Machine Learning for Auditors: Automating Fraud Investigations Through Artificial Intelligence
- Length: 259 pages
- Edition: 1
- Language: English
- Publisher: Apress
- Publication Date: 2022-03-13
- ISBN-10: 1484280504
- ISBN-13: 9781484280508
- Sales Rank: #0 (See Top 100 Books)
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.
Table of Contents About the Author About the Technical Reviewer Introduction Chapter 1: Three Lines of Defense AI, ML, and Auditing The Three Lines of Defense Model Risk Management Complexities The Three Lines Model Conclusion Chapter 2: Common Audit Challenges Data Literacy Manual Testing Data Sources Structured vs. Unstructured Data Citizen Developers Data Wrangling Data Bias Conclusion Chapter 3: Existing Solutions Substantive Testing CAATs Fit-for-Purpose Technologies Process Mining Continuous Auditing Conclusion Chapter 4: Data Analytics CRISP-DM Data Analytics Audit Applications Data Analytics vs. Data Science Conclusion Chapter 5: Analytics Structure and Environment Analytics Organization Structure Organization Climate The Role of Senior Leaders Conclusion Chapter 6: Introduction to AI, Data Science, and Machine Learning A Self-Driving Car Components of an AI System CRISP-DM for Data Science Domain Knowledge Payment Fraud/Anomaly Detection Conclusion Chapter 7: Myths and Misconceptions Myth #1: You Need an Advanced Degree to Be a Data Scientist Myth #2: Correlation Implies Causation Myth #3: The Model Building Is the Most Critical Step Conclusion Chapter 8: Trust, but Verify What Is Trust, but Verify? Why Is It Important to Verify? Integrated Reporting Conclusion Chapter 9: Machine Learning Fundamentals Supervised Learning Classifiers Decision Trees Random Forests Support Vector Machines Logistic Regression Naive Bayes Deep Learning Confusion Matrix ROC Curves Regression Linear Regression Unsupervised Learning Clustering Algorithms k-means Clustering Hierarchical Clustering Silhouette Score Elbow Plot Dimensionality Reduction Curse of Dimensionality Principal Component Analysis Scree Plots Overfitting, Underfitting, and Feature Extraction Overfitting Underfitting Feature Extraction Ensemble Conclusion Chapter 10: Data Lakes Introduction to Data Lakes Tangible Value Role as Analytics Enabler Architectures Conclusion Chapter 11: Leveraging the Cloud Local Workstation Cloud Computing Amazon SageMaker Google Colab IBM Watson Conclusion Chapter 12: SCADA and Operational Technology Fourth Industrial Revolution SCADA Auditing Applying AI to SCADA Auditing Conclusion Chapter 13: What Is Storytelling? Data Storytelling Common Pitfalls Misleading Graphs Anscombe’s Quartet Engaging the Audience Conclusion Chapter 14: Why Storytelling? Why Does It Work? General Guidelines of Good Storytelling General Dashboard Layout Conclusion Chapter 15: When to Use Storytelling? Use Stories to Inspire or Motivate an Action When Can We Use Storytelling? Less Is More Conclusion Chapter 16: Types of Visualizations Basic Visuals Advanced Visuals One-Hot Encoding Conclusion Chapter 17: Effective Stories Case Study: “The Best Stats You’ve Ever Seen” Case Study: “U.S. GUN KILLINGS IN 2018” Case Study: “Numbers of Different Magnitudes” Recap of Effective Storytelling Elements Conclusion Chapter 18: Storytelling Tools Technical Expertise Available Tools Qlik Power BI Tableau Mode Analytics Conclusion Chapter 19: Storytelling in Auditing Audit Use Cases Communication of Findings Support Recommendations Clarify Business Knowledge Conclusion Chapter 20: How to Use the Recipes What Is a Recipe? Prerequisites Where Can You Find the Python Code? Implementation Considerations Conclusion Chapter 21: Fraud and Anomaly Detection The Dish: A Fraud and Anomaly Detection System Ingredients Instructions Step 1: Data Preparation Step 2: Exploratory Data Analysis Step 3: Apply Interquartile Range (IQR) Method Step 4: Perform Supervised Learning Step 5: Perform Unsupervised Learning Analysis Step 6: Review Exceptions with Additional Data Step 7: Re-evaluate the Models Variation and Serving Chapter 22: Access Management The Dish: ERP Access Management Audit Ingredients Instructions Step 1: Data Preparation Step 2: Exploratory Data Analysis Step 3: Scatter Plot of ERP Access Step 4: Review Exceptions with Additional Data Step 5: Reperform the Analysis Variation and Serving Chapter 23: Project Management The Dish: Project Portfolio Analysis Ingredients Instructions Step 1: Data Preparation Step 2: Exploratory Data Analysis Step 3: Perform Random Forest Classification Step 4: Review Feature Importance Variation and Serving Conclusion Chapter 24: Data Exploration The Dish: Understanding the Data Through Exploration Ingredients Instructions Step 1: Data Preparation Step 2: Exploratory Data Analysis Variation and Serving Conclusion Chapter 25: Vendor Duplicate Payments The Dish: Vendor Duplicate Payments Analysis Ingredients Instructions Step 1: Data Preparation Step 2: Perform K-NN Algorithm Step 3: Review Exceptions with Additional Data Variation and Serving Conclusion Chapter 26: CAATs 2.0 The Dish: CAATs Analysis Using ML Ingredients Instructions Step 1: Data Preparation Step 2: Exploratory Data Analysis Step 3: K-Means Clustering Variation and Serving Conclusion Chapter 27: Log Analysis The Dish: NLP Log Analysis Ingredients Instructions Step 1: Data Preparation Step 2: Exploratory Data Analysis Step 3: Perform Topic Modeling Step 4: Reperform the Analysis Variation and Serving Conclusion Chapter 28: Concluding Remarks Index
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