Description
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.
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.
Book Information
ISBN 9781484280508
Author Maris Sekar
Format Paperback
Page Count 242
Imprint APress
Publisher APress