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Book cover
  • Book
  • © 2022

Machine Learning for Auditors

Automating Fraud Investigations Through Artificial Intelligence

Apress

Authors:

  1. Maris Sekar

    1. Calgary, Canada

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  • Enables a deeper understanding of your organizational processes and data

  • Provides valuable skills around storytelling and data analysis techniques

  • Improves data literacy in the audit team and the organization

Sections

eBook USD 39.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-8051-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 49.99
Price excludes VAT (USA)
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This is a preview of subscription content, access via your institution.

Table of contents (28 chapters)

  1. Front Matter

    Pages i-xvii
    PDF

  2. Trusted Advisors

    1. Front Matter

      Pages 1-1
      PDF

    2. Three Lines of Defense

      • Maris Sekar
      Pages 3-12

    3. Common Audit Challenges

      • Maris Sekar
      Pages 13-31

    4. Existing Solutions

      • Maris Sekar
      Pages 33-42

    5. Data Analytics

      • Maris Sekar
      Pages 43-48

    6. Analytics Structure and Environment

      • Maris Sekar
      Pages 49-53

  3. Understanding Artificial Intelligence

    1. Front Matter

      Pages 55-55
      PDF

    3. Myths and Misconceptions

      • Maris Sekar
      Pages 73-76

    4. Trust, but Verify

      • Maris Sekar
      Pages 77-87

    5. Machine Learning Fundamentals

      • Maris Sekar
      Pages 89-116

    6. Data Lakes

      • Maris Sekar
      Pages 117-122

    7. Leveraging the Cloud

      • Maris Sekar
      Pages 123-130

    8. SCADA and Operational Technology

      • Maris Sekar
      Pages 131-135

  4. Storytelling

    1. Front Matter

      Pages 137-137
      PDF

    2. What Is Storytelling?

      • Maris Sekar
      Pages 139-145

    3. Why Storytelling?

      • Maris Sekar
      Pages 147-150

    4. When to Use Storytelling?

      • Maris Sekar
      Pages 151-154

    5. Types of Visualizations

      • Maris Sekar
      Pages 155-167
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About this book

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. 
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Keywords

  • Auditng
  • Storytelling
  • Trusted Advisors
  • Predictive Models
  • Data Analytics
  • Fraud Detection
  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Data Science
  • Internal Auditing
  • Lines of Defense
  • Anomaly Detection
  • Access Management
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Authors and Affiliations

  • Calgary, Canada

    Maris Sekar

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About the author

​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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Bibliographic Information

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Access via your institution
eBook USD 39.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-8051-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 49.99
Price excludes VAT (USA)
Learn about institutional subscriptions