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Machine Learning for Auditors

Automating Fraud Investigations Through Artificial Intelligence

  • Book
  • © 2022

Overview

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

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Table of contents (28 chapters)

  1. Storytelling

    1. Effective Stories

      • Maris Sekar
      Pages 169-172

    2. Storytelling Tools

      • Maris Sekar
      Pages 173-179

    3. Storytelling in Auditing

      • Maris Sekar
      Pages 181-183

  2. Implementation Recipes

    1. Front Matter

      Pages 185-185
      Download chapter PDF

    2. How to Use the Recipes

      • Maris Sekar
      Pages 187-191

    3. Fraud and Anomaly Detection

      • Maris Sekar
      Pages 193-202

    4. Access Management

      • Maris Sekar
      Pages 203-208

    5. Project Management

      • Maris Sekar
      Pages 209-214

    6. Data Exploration

      • Maris Sekar
      Pages 215-218

    7. Vendor Duplicate Payments

      • Maris Sekar
      Pages 219-221

    8. CAATs 2.0

      • Maris Sekar
      Pages 223-227

    9. Log Analysis

      • Maris Sekar
      Pages 229-233

    10. Concluding Remarks

      • Maris Sekar
      Pages 235-236

  3. Back Matter

    Pages 237-242
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Keywords

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. 

Authors and Affiliations

  • Calgary, Canada

    Maris Sekar

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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