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001444715 001__ 1444715
001444715 003__ OCoLC
001444715 005__ 20230310003724.0
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001444715 019__ $$a1301452969$$a1301487853$$a1301773245$$a1301905203$$a1301944846$$a1302002946$$a1302105975$$a1302119477$$a1302180778
001444715 020__ $$a9781484280515$$q(electronic bk.)
001444715 020__ $$a1484280512$$q(electronic bk.)
001444715 020__ $$z1484280504
001444715 020__ $$z9781484280508
001444715 0247_ $$a10.1007/978-1-4842-8051-5$$2doi
001444715 0248_ $$a9781484280515
001444715 035__ $$aSP(OCoLC)1301273982
001444715 040__ $$aORMDA$$beng$$erda$$epn$$cORMDA$$dYDX$$dEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dN$T$$dOCLCF$$dK6U$$dIAC$$dOCLCQ$$dUKAHL
001444715 049__ $$aISEA
001444715 050_4 $$aHF5668.25
001444715 08204 $$a657.0285/631$$223
001444715 1001_ $$aSekar, Maris,$$eauthor.
001444715 24510 $$aMachine learning for auditors :$$bautomating fraud investigations through artificial intelligence /$$cMaris Sekar.
001444715 24630 $$aAutomating fraud investigations through artificial intelligence
001444715 250__ $$a[First edition].
001444715 264_1 $$aNew York, NY :$$bApress,$$c[2022]
001444715 300__ $$a1 online resource (241 pages)
001444715 336__ $$atext$$btxt$$2rdacontent
001444715 337__ $$acomputer$$bc$$2rdamedia
001444715 338__ $$aonline resource$$bcr$$2rdacarrier
001444715 500__ $$aIncludes index.
001444715 5050_ $$aPart I. Trusted Advisors -- 1. Three Lines of Defense -- 2. Common Audit Challenges -- 3. Existing Solutions -- 4. Data Analytics -- 5. Analytics Structure & Environment -- Part II. Understanding Artificial Intelligence -- 6. Introduction to AI, Data Science, and Machine Learning -- 7. Myths and Misconceptions -- 8. Trust, but Verify -- 9. Machine Learning Fundamentals -- 10. Data Lakes -- 11. Leveraging the Cloud -- 12. SCADA and Operational Technology -- Part III. Storytelling -- 13. What is Storytelling? -- 14. Why Storytelling? -- 15. When to Use Storytelling -- 16. Types of Visualizations -- 17. Effective Stories -- 18. Storytelling Tools -- 19. Storytelling in Auditing -- Part IV. Implementation Recipes -- 20. How to Use the Recipes -- 21. Fraud and Anomaly Detection -- 22. Access Management -- 23. Project Management -- 24. Data Exploration -- 25. Vendor Duplicate Payments -- 26. CAATs 2.0 -- 27. Log Analysis -- 28. Concluding Remarks.
001444715 506__ $$aAccess limited to authorized users.
001444715 520__ $$aUse 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.
001444715 588__ $$aDescription based on print version record.
001444715 650_0 $$aAuditing, Internal$$xData processing.
001444715 650_0 $$aCorporations$$xAccounting$$xData processing.
001444715 650_0 $$aFraud$$xPrevention.
001444715 650_0 $$aMachine learning.
001444715 650_6 $$aVérification interne$$xInformatique.
001444715 650_6 $$aApprentissage automatique.
001444715 655_0 $$aElectronic books.
001444715 77608 $$iPrint version:$$z1484280504$$z9781484280508$$w(OCoLC)1290431072
001444715 852__ $$bebk
001444715 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8051-5$$zOnline Access$$91397441.1
001444715 909CO $$ooai:library.usi.edu:1444715$$pGLOBAL_SET
001444715 980__ $$aBIB
001444715 980__ $$aEBOOK
001444715 982__ $$aEbook
001444715 983__ $$aOnline
001444715 994__ $$a92$$bISE