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Title
Machine learning for auditors : automating fraud investigations through artificial intelligence / Maris Sekar.
Edition
[First edition].
ISBN
9781484280515 (electronic bk.)
1484280512 (electronic bk.)
1484280504
9781484280508
Published
New York, NY : Apress, [2022]
Language
English
Description
1 online resource (241 pages)
Other Standard Identifiers
10.1007/978-1-4842-8051-5 doi
9781484280515
Call Number
HF5668.25
Dewey Decimal Classification
657.0285/631
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.
Note
Includes index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Available in Other Form
Print version: 9781484280508
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.
