Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes.

Data analytics and emerging technology tools continue to evolve the business world, and employers expect new skillsets from graduates. Prepare your students to meet the rapidly changing demands of the workforce and become the future auditors and accounting professionals of tomorrow with Auditing: A Practical Approach with Data Analytics, 2nd Edition. In order to develop job-ready skills, students need to have a thorough understanding of auditing applications and procedures. Auditing, 2nd Edition helps students learn core auditing concepts efficiently and spark effective learning through integrated assessment learning that builds students' confidence and strengthens their ability to make connections between topics and real-world application. Throughout the course, students work through a practical, case-based approach with a decision-making focus, all within a real-world context with the Cloud 9 continuing case, Audit Decision Cases, and Audit Decision-Making Examples. These cases and resources help students learn to think critically within the auditing context and refine the professional judgement and communication skills needed to make real business decisions auditors face every day. With Auditing: A Practical Approach with Data Analytics you will be able to help students develop a deeper understanding of auditing procedures and learn how to perform a real-world audit, stay up-to-date on the latest audit standards technology tools, and develop the key skills to become the auditors of tomorrow.

This book examines (1) concentration in the market for public company audits, (2) the potential for smaller accounting firms' growth to ease market concentration, and (3) proposals that have been offered by others for easing concentration and the barriers facing smaller firms in expanding their market shares.

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.

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.

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.

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. 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. Using R and RStudio, the book 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.

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

​This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.