Guide To Audit Data Analytics

Designed to facilitate the use of audit data analytics (ADAs) in the financial statement audit, this title was developed by leading experts across the profession and academia. The guide defines audit data analytics as “the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit.” Simply put, ADAs can be used to perform a variety of procedures to gather audit evidence. Each chapter focuses on an audit area and includes step-by-step guidance illustrating how ADAs can be used throughout the financial statement audit. Suggested considerations for assessing the reliability of data are also included in a separate appendix.

This is a two-volume set. Guide to Data Analytics: Designed to facilitate the use of audit data analytics (ADAs) in the financial statement audit, this title was developed by leading experts across the profession and academia. The guide defines audit data analytics as "the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit." Simply put, ADAs can be used to perform a variety of procedures to gather audit evidence. Each chapter focuses on an audit area and includes step-by-step guidance illustrating how ADAs can be used throughout the financial statement audit. Suggested considerations for assessing the reliability of data are also included in a separate appendix. Analytical Procedures 2017: Updated as of October 1, 2017, This book includes relevant guidance contained in applicable standards and other technical sources. The guide summarizes applicable requirements and practices and delivers how-to advice for handling analytical procedures used in audits. The included case study illustrates the use of analytical procedures in planning and substantive testing, utilizing trend analysis, ratio analysis, reasonableness testing, and regression analysis. The remaining appendixes include examples of financial ratios and a comparison of the key provisions of the risk assessment standards.

AICPA Audit Guides are developed and updated to provide guidance and tools for practitioners as they perform audit engagements. The AICPA Audit Guide Analytical Procedures, updated as of March 1, 2012, includes relevant guidance contained in applicable standards and other technical sources. The guide summarizes applicable requirements and practices, and delivers "how-to" advice for handling analytical procedures used in audits. The included case study illustrates the use of analytical procedures in planning and substantive testing, utilizing trend analysis, ratio analysis, reasonableness testing, and regression analysis. The appendices include a variety of examples of financial ratios and a comparison of the key provisions of the Risk Assessment Standards. This AICPA Audit Guide has been fully conformed to reflect the new standards resulting from the Clarity Project. Fully incorporating the clarified auditing standards into this year’s edition of the guide enables auditors to further their understanding of these recently issued auditing standards, as well as begin updating their audit methodologies, resources, and tools prior to the clarified auditing standards’ effective date. Additionally, this approach gives auditors the opportunity to review and understand the changes made by their third-party audit methodology and resource providers, if applicable. The clarified auditing standards are effective for audits of financial statements for periods ending on or after December 15, 2012 (calendar year 2012 audits). Auditors should continue to use the previous edition of this guide (2008 edition) until the clarified auditing standards become effective for the auditors’ engagements.

Designed to facilitate the use of audit data analytics (ADAs) in the financial statement audit, this title was developed by leading experts across the profession and academia. The guide defines audit data analytics as “the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit.” Simply put, ADAs can be used to perform a variety of procedures to gather audit evidence. Each chapter focuses on an audit area and includes step-by-step guidance illustrating how ADAs can be used throughout the financial statement audit. Suggested considerations for assessing the reliability of data are also included in a separate appendix.

Updated as of October 1, 2017, This bookincludes relevant guidance contained in applicable standards and other technical sources. The guide summarizes applicable requirements and practices and delivers how-to advice for handling analytical procedures used in audits. The included case study illustrates the use of analytical procedures in planning and substantive testing, utilizing trend analysis, ratio analysis, reasonableness testing, and regression analysis. The remaining appendixes include examples of financial ratios and a comparison of the key provisions of the risk assessment standards.

This book is about how to reduce large data sets down to the critical few high-risk subsets.

The explosion of data analytics in the auditing profession demands a different kind of auditor. Auditing: A Practical Approach with Data Analytics prepares students for the rapidly changing demands of the auditing profession by meeting the data-driven requirements of today's workforce. Because no two audits are alike, this course uses a practical, case-based approach to help students develop professional judgement, think critically about the auditing process, and develop the decision-making skills necessary to perform a real-world audit. To further prepare students for the profession, this course integrates seamless exam review for successful completion of the CPA Exam.

This book, using R and RStudio, demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more. Today, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control. Additionally, a firm’s data—particularly in the case of finance, software, insurance and biotech firms—comprises most of the audited value of the firm. Financial audits are critical mechanisms for ensuring the integrity of information systems and the reporting of organizational finances. They help avoid the abuses that led to passage of legislation such as the Foreign Corrupt Practices Act (1977), and the Sarbanes-Oxley Act (2002). Audit effectiveness has declined over the past two decades, as auditor skillsets have failed to keep up with advances in information technology. Information and communication technology lie at the core of commerce today and are integrated in business processes around the world. This book is designed to meet the increasing need of audit professionals to understand information technology and the controls required to manage it. This 2nd edition includes updated code and test. Machine learning, AI, and SEC’s EDGAR data are also, improved and updated. The material included focuses on the requirements for annual Securities and Exchange Commission audits (10-K) for listed corporations. These represent the benchmark auditing procedures for specialized audits, such as internal, governmental, and attestation audits. Many examples reflect the focus of the 2024 CPA exam, and the data analytics-machine learning approach will be central to the AICPA’s programs, in the near future.