Machine Learning For Auditors

Download or read book Machine Learning for Auditors written by Maris Sekar and published by Apress. This book was released on 2022-02-27 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings. Machine Learning for Auditors provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization. What You Will Learn Understand the role of auditors as trusted advisors Perform exploratory data analysis to gain a deeper understanding of your organization Build machine learning predictive models that detect fraudulent vendor payments and expenses Integrate data analytics with existing and new technologies Leverage storytelling to communicate and validate your findings effectively Apply practical implementation use cases within your organization Who This Book Is For AI Auditing is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes.

Download or read book Machine Learning Applications for Accounting Disclosure and Fraud Detection written by Papadakis, Stylianos and published by IGI Global. This book was released on 2020-10-02 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.

Download or read book Artificial Intelligence for Audit, Forensic Accounting, and Valuation written by Al Naqvi and published by John Wiley & Sons. This book was released on 2020-08-25 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strategically integrate AI into your organization to compete in the tech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform accounting and auditing professions, yet its current application within these areas is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation accounting. Artificial Intelligence for Audit, Forensic Accounting, and Valuation provides a strategic viewpoint on how AI can be comprehensively integrated within audit management, leading to better automated models, forensic accounting, and beyond. No other book on the market takes such a wide-ranging approach to using AI in audit and accounting. With this guide, you’ll be able to build an innovative, automated accounting strategy, using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for audit and accounting firms. With better AI comes better results. If you aren’t integrating AI and automation in the strategic DNA of your business, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of integrated, automated audit and accounting services Learn how to build AI into your organization to remain competitive in the era of automation Go beyond siloed AI implementations to modernize and deliver results across the organization Understand and overcome the governance and leadership challenges inherent in AI strategy Accounting and auditing firms need a comprehensive framework for intelligent, automation-centric modernization. Artificial Intelligence for Audit, Forensic Accounting, and Valuation delivers just that—a plan to evolve legacy firms by building firmwide AI capabilities.

Download or read book Robo-Auditing written by Patrick J. D. Taylor and published by Lioncrest Publishing. This book was released on 2018-07-10 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: As CFO of a large company, you might have considered adding an artificial intelligence system into your financial operations to increase efficiency, boost profits, reduce waste, and detect fraud. Only you're afraid it might be too costly and complicated. Robo-Auditing can ease your fears, providing everything you need to know about this thrilling, cutting edge technology. As an engineer with an MBA, Patrick Taylor is uniquely qualified to demystify A.I. and demonstrate its many benefits. In this extraordinary, must-read handbook, he offers essential guidelines and information to help you: - Understand how A.I. works - Incorporate "robo-auditors" into existing financial networks - Train your team to use the technology effectively - And more Implementing an A.I. system doesn't have to be difficult, intimidating, or prohibitively expensive, and it can make an enormous difference in your day-to-day operations. Robo-Auditing is your passport into the exciting future of corporate finance.

Download or read book Artificial Intelligence in Accounting and Auditing written by Miklos A. Vasarhelyi and published by Berg Publishers. This book was released on 1988 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: A study of artificial intelligence in accounting and auditing. Topics addressed include: expert systems for audit tasks; REA accounting database evolution; fuzzy logic - treating the uncertainty in expert systems; bankruptcy prediction via a recursive partitioning model; and more.

Download or read book The Essentials of Machine Learning in Finance and Accounting written by Mohammad Zoynul Abedin and published by Routledge. This book was released on 2021-06-20 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

Download or read book The Soar Cognitive Architecture written by John E. Laird and published by MIT Press. This book was released on 2012-04-13 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.