東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Business Analytics." ]

切換: 標籤 | MARC模式 | ISBD

Machine learning for auditors = auto...

Sekar, Maris.

FindBook

Google Book

Amazon

博客來

Machine learning for auditors = automating fraud investigations through artificial intelligence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for auditors/ by Maris Sekar.
其他題名:	automating fraud investigations through artificial intelligence /
作者:	Sekar, Maris.
出版者:	Berkeley, CA :Apress : : 2022.,
面頁冊數:	xvii, 242 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Auditing, Internal - Data processing. -
電子資源:	https://doi.org/10.1007/978-1-4842-8051-5
ISBN:	9781484280515

Machine learning for auditors = automating fraud investigations through artificial intelligence /
Sekar, Maris.

Machine learning for auditorsautomating fraud investigations through artificial intelligence /[electronic resource] :by Maris Sekar. - Berkeley, CA :Apress :2022. - xvii, 242 p. :ill., digital ;24 cm.

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.

ISBN: 9781484280515

Standard No.: 10.1007/978-1-4842-8051-5doiSubjects--Topical Terms:

1641122
Auditing, Internal
--Data processing.

LC Class. No.: HF5668.25 / .S45 2022

Dewey Class. No.: 657.0285631

Machine learning for auditors = automating fraud investigations through artificial intelligence /
LDR:03754nmm a2200325 a 4500 001 2299400
003 DE-He213
005 20220226065833.0
006 m d
007 cr nn 008maaau
008 230324s2022 cau s 0 eng d
020 $a 9781484280515 $q (electronic bk.)
020 $a 9781484280508 $q (paper)
024 7 $a 10.1007/978-1-4842-8051-5 $2 doi
035 $a 978-1-4842-8051-5
040 $a GP $c GP
041 0 $a eng
050 4 $a HF5668.25 $b .S45 2022
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 657.0285631 $2 23
090 $a HF5668.25 $b .S463 2022
100 1 $a Sekar, Maris. $3 3596833
245 1 0 $a Machine learning for auditors $h [electronic resource] : $b automating fraud investigations through artificial intelligence / $c by Maris Sekar.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xvii, 242 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Auditing, Internal $x Data processing. $3 1641122
650 0 $a Corporations $x Accounting $x Data processing. $3 3510505
650 0 $a Fraud $x Prevention. $3 713062
650 0 $a Machine learning. $3 533906
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Business Analytics. $3 3592362
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-8051-5
950 $a Professional and Applied Computing (SpringerNature-12059)