Machine learning for auditors : automating fraud investigations through artificial intelligence / Maris Sekar.

Format:

Book

Author/Creator:

Sekar, Maris, author.

Language:

English

Subjects (All):

Auditing, Internal--Data processing.

Auditing, Internal.

Corporations--Accounting--Data processing.

Corporations.

Fraud--Prevention.

Fraud.

Machine learning.

Physical Description:

1 online resource (241 pages)

Edition:

[First edition].

Place of Publication:

New York, New York : Apress Media LLC, [2022]

Summary:

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.

Contents:

Part I. Trusted Advisors

1. Three Lines of Defense

2. Common Audit Challenges

3. Existing Solutions

4. Data Analytics

5. Analytics Structure & Environment

Part II. Understanding Artificial Intelligence

6. Introduction to AI, Data Science, and Machine Learning

7. Myths and Misconceptions

8. Trust, but Verify

9. Machine Learning Fundamentals

10. Data Lakes

11. Leveraging the Cloud

12. SCADA and Operational Technology

Part III. Storytelling

13. What is Storytelling?

14. Why Storytelling?

15. When to Use Storytelling

16. Types of Visualizations

17. Effective Stories

18. Storytelling Tools

19. Storytelling in Auditing

Part IV. Implementation Recipes

20. How to Use the Recipes

21. Fraud and Anomaly Detection

22. Access Management

23. Project Management

24. Data Exploration

25. Vendor Duplicate Payments

26. CAATs 2.0

27. Log Analysis

28. Concluding Remarks.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Includes index.

Other Format:

Print version: Sekar, Maris Machine Learning for Auditors

ISBN:

9781484280515

1484280512

OCLC:

1301273982

Publisher Number:

9781484280515