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.

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.

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.

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.

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.

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

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.

Master new, disruptive technologies in the field of auditing Agile Auditing: Fundamentals and Applications introduces readers to the applications and techniques unlocked by tested and proven agile project management principles. This book educates readers on an approach to auditing that emphasizes risk-based auditing, collaboration, and speedy delivery of meaningful assurance assessments while ensuring quality results and a focus on the areas that pose the greatest material risks to the business under audit. The discipline of auditing has been forever changed via the introduction of new technologies, including: Machine learning Virtual Conferencing Process automation Data analytics Hugely popular in software development, the agile approach is just making its way into the field of audit. This book provides concrete examples and practical solutions for auditors who seek to implement agile techniques and methods. Agile Auditing is perfect for educators, practitioners, and students in the auditing field who are looking for ways to introduce greater levels of efficiency and effectiveness to their discipline.

Operational Auditing: Principles and Techniques for a Changing World, 2nd edition, explains the proven approaches and essential procedures to perform risk-based operational audits. It shows how to effectively evaluate the relevant dynamics associated with programs and processes, including operational, strategic, technological, financial and compliance objectives and risks. This book merges traditional internal audit concepts and practices with contemporary quality control methodologies, tips, tools and techniques. It explains how internal auditors can perform operational audits that result in meaningful findings and useful recommendations to help organizations meet objectives and improve the perception of internal auditors as high-value contributors, appropriate change agents and trusted advisors. The 2nd edition introduces or expands the previous coverage of: • Control self-assessments. • The 7 Es framework for operational quality. • Linkages to ISO 9000. • Flowcharting techniques and value-stream analysis • Continuous monitoring. • The use of Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs). • Robotic process automation (RPA), artificial intelligence (AI) and machine learning (ML); and • Adds a new chapter that will examine the role of organizational structure and its impact on effective communications, task allocation, coordination, and operational resiliency to more effectively respond to market demands.