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Cover: 9781484280508 | Machine Learning for Auditors | Maris Sekar | Taschenbuch | Paperback
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Machine Learning for Auditors
Automating Fraud Investigations Through Artificial Intelligence
Taschenbuch von Maris Sekar
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
¿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.
Zusammenfassung

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

Inhaltsverzeichnis
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

Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xvii
242 S.
95 s/w Illustr.
242 p. 95 illus.
ISBN-13: 9781484280508
ISBN-10: 1484280504
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Sekar, Maris
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 15 mm
Von/Mit: Maris Sekar
Erscheinungsdatum: 27.02.2022
Gewicht: 0,496 kg
Artikel-ID: 120946395
Über den Autor
¿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.
Zusammenfassung

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

Inhaltsverzeichnis
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

Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xvii
242 S.
95 s/w Illustr.
242 p. 95 illus.
ISBN-13: 9781484280508
ISBN-10: 1484280504
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Sekar, Maris
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 15 mm
Von/Mit: Maris Sekar
Erscheinungsdatum: 27.02.2022
Gewicht: 0,496 kg
Artikel-ID: 120946395
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