The Use Of Data Analytics In Internal Audit To Improve Decision Making

The purpose of this dissertation was to examine how managers' judgments from an internal auditor's recommendation are influenced by some aspects of newer data sources and the related visualizations. This study specifically examined how managers' judgments from an internal auditor's recommendation are influenced by the (1) supportiveness of non-financial data with the internal auditor's recommendation and (2) evaluability of visual representations for non-financial data used to communicate the recommendation. This was investigated in a setting where financial data does not support the internal auditor's recommendation. To test my hypotheses, I conducted an experiment that uses an inventory write-down task to examine the likelihood that a manager agrees with an internal auditor's inventory write-down recommendation. This task was selected as it requires making a prediction and both financial and newer non-financial data sources are relevant to inform this judgment. The study was conducted with MBA students who proxy for managers in organizations. Evaluability of visual representations was operationalized as the (1) proximity of financial and non-financial graphs, and (2) type of non-financial graph as requiring a length judgment or not. This dissertation contributes to accounting literature and the internal auditing profession. First, I contribute to recent experimental literature on data analytics by providing evidence that newer non-financial data sources will be integrated into managers' judgments even when financial data is inconsistent. However, I also identified that the effectiveness of appropriate agreement with an internal auditor's recommendation depends on the evaluability of the visualizations for non-financial data. Second, I expand on the literature that examines managers' agreement with recommendations from internal auditors by examining an unexplored yet relevant context of using newer non-financial data sources and communicating these results. Specifically, I identified how the evaluability of visual representations for non-financial data interacts with the supportiveness of non-financial data with the internal auditor's recommendation to create differences in managers' agreement with the recommendation. I also identified confidence in the internal auditor's recommendation as an explanatory variable in some situations. My findings also have practical value for the internal auditing profession to understand the importance of appropriate visualizations in audit reporting.

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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.

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.

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.

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 presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.

Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.

The authors, Arslan Khan and Edward Zimmer, demystify any perceived complexities in establishing a robust analytics control function. They provide a simple process to develop and implement an audit analytics strategy to meet the specific needs of organizations. This book provides practical approaches to using data analytics to enable continuous auditing to monitor the effectiveness of business controls. The authors' experience in developing data analytics that support an Internal Audit function can also be leveraged in other areas of the organization. Through the use of simple examples and practical tips, a framework for developing a sustainable audit analytics strategy is provided. Specific guidance is also provided regarding the talent, processes, and technology needed to move from your current state to the target environment. The business case has moved from "can the organization afford an audit analytics function" to "can an organization afford NOT to have an audit analytics function."