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.

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

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.

There are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitioner’s viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.