JCAATs - Data Analysis and Smart Audit (Attached：Trial version software + Dataset)

　　◎The world's first general audit software with AI, designed to run on both Mac and Windows OS.

　　With the advent of the Al era, professionals need to select the right tools to meet new opportunities and challenges! This instructional material focuses on the application of big data analysis, text mining, and machine learning in auditing to assist readers in understanding the latest developments in data analysis and smart audit.

　　JCAATs is a new generation audit software developed by Python, which can be executed in a Windows and MAC operation systems with a multi-language, visual, and easy-to-use interface. Non-IT professionals can easily conduct smart data analysis. Through JCAATs' built-in data fusion technology and OPEN DATA connector, heterogeneous data can be quickly analyzed in real-time and effectively identifying anomalies.
   
　　The features of this book include:

　　The book is structured to provide a progressive learning experience, with chapters covering different concepts and applications, as well as exercises and practice questions.

　　The official textbook endorsed by ICAEA for the ICCP (International Certified CAATs Practitioner) certification exams.

　　This book provides an educational version of the JCAATs - AI audit software for trial use, with a multi-language interface.

　　The book provides several practical examples which allowing you to hands-on practice for smart auditing. This material is suitable for professionals such as accountants, auditors, compliance officers, risk managers, information security professionals, managers at all levels, and college students as a learning reference.

　　To cultivate AI programming skills in business schools and other institutions, the International Computer Auditing Education Association (ICAEA) recommends starting with No Code. This approach allows students to develop audit applications to solve real-world problems without needing to write any code, giving them practical experience and enhancing their professional value. Once proficient in No Code, students can progress to Read Code training, where they gain an understanding of coding logic. Finally, Write Code training enables them to create their own AI applications. This approach prepares students for a future working environment centered on data analysis and smart auditing, where they can collaborate with AI auditing robots. As the AI era presents new challenges to the education of business professionals, colleges must adopt innovative and effective approaches to prepare their students for the workforce of the future.

 

作者簡介

Sherry Huang

　　She is a CEO of Jacksoft Ltd. which is one of top audit technology innovative company in Asia. Sherry has more than 20 years of experience in the IT service industry, providing consultancy services to over 500 enterprises in the audit industry. She currently serves as the chair of the ICAEA Taiwan chapter and previously held the position of director of professional development for IIA Taiwan. Sherry also has extensive experience serving as Chief of Internal Auditor and Chief of Accounting for a public company in Taiwan. Before that, she worked as a Senior Auditor at KPMG. Sherry is widely recognized for inventing JCAATs, which were first released in London in 2017. She holds an MBA and various professional certifications, including CIA, CCSA, ICCP, CEAP, CFAP, ISO 14067:2018, ISO27001, ACDA, etc. 

Part 1: Text Book + Exercise + Case Studies
Chapter 1 Introduction to Data Analysis and Smart Audit
Chapter 2 Overview of JCAATs - AI Audit software
Chapter 3 Project
Chapter 4 Data
Chapter 5 Expressions
Chapter 6 Validation
Chapter 7 Analysis
Chapter 8 Script
Chapter 9 Text Mining
Chapter 10 Machine Learning
Chapter 11 Reporting
Chapter 12 Sampling
Chapter 13 Tools
 
Part 2:  Exercise Solution + Practice
Chapter 3 Exercise solutions for Project
Chapter 4 Exercise solutions for Data
Chapter 5 Exercise solutions for Expressions
Chapter 6 Exercise solutions for Validation
Chapter 7 Exercise solutions for Analysis
Chapter 8 Exercise solutions for Script
Chapter 9 Exercise solutions for Text Mining
Chapter 10 Exercise solutions for Machine Learning
Chapter 11 Exercise solutions for Reporting
Chapter 12 Exercise solutions for Sampling
Chapter 13 Exercise solutions for Tools
Appendix A References
Appendix B Other Learning Resources