01329nam0-2200397---450-99000096443020331620091202121438.088-14-06068-10096443USA010096443(ALEPH)000096443USA01009644320020218d1996----km-y0ITAy01------baitaIT||||||||001yyCodice del nuovo contenzioso tributarioil D. lgs. 546/1992, aggiornato con la Legge 24 ottobre 1996, n. 556, commentato articolo per articolo ed annotato con la giurisprudenzaCarlo ThomasMilanoGiuffré1996VIII, 392 p24 cmCosa & comeTributi2001Cosa & comeTributiContenzioso tributarioProcesso tributario343.45040269THOMAS,Carlo407606ITsalbcISBD990000964430203316XXX.B. Coll. 13/ 259 (IRA 40 151)12698 GXXX.B. Coll. 13/ (IRA)00128522BKGIUPATTY9020020218USA01115220020403USA011739PATRY9020040406USA011706RSIAV19020091202USA011214CODICE del nuovo contenzioso tributario657373UNISA00901nlm 2200253Ia 450 99641124750331620210420123006.019831111d1661---- uy |engdrcnuSwallowan almanack for the year of our Lord God 1661 : being the first after bissextile or leap year, and from the worlds creation 5664 : calculated properly for the meridian of the universitie and town of Cambridge ..Cambridge [Cambridgeshire]Printed by John Field1661Testo elettronico (PDF) ([40] p. : ill.)Base dati testualeRiproduzione dell'originale nella British Library.AstrologiaBNCF133.5SWALLOW,John498157cbaITcbaREICAT996411247503316EBERSwallow1772434UNISA04879nam 2200577 450 991054817680332120221006152107.01-4842-8051-210.1007/978-1-4842-8051-59781484280515(MiAaPQ)EBC6896984(Au-PeEL)EBL6896984(CKB)21325604100041(OCoLC)1301273982(OCoLC-P)1301273982(CaSebORM)9781484280515(PPN)260832685(EXLCZ)992132560410004120221006d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine learning for auditors automating fraud investigations through artificial intelligence /Maris Sekar[First edition].New York, New York :Apress Media LLC,[2022]©20221 online resource (241 pages)Includes index.Print version: Sekar, Maris Machine Learning for Auditors Berkeley, CA : Apress L. P.,c2022 9781484280508 Includes bibliographical references and index.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.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.Auditing, InternalData processingCorporationsAccountingData processingFraudPreventionMachine learningAuditing, InternalData processing.CorporationsAccountingData processing.FraudPrevention.Machine learning.657.0285631Sekar Maris1208941MiAaPQMiAaPQMiAaPQBOOK9910548176803321Machine Learning for Auditors2789086UNINA