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| artificial intelligence and machine learning in accounting: Artificial Intelligence in Accounting Cory Ng, John Alarcon, 2020-12-08 Artificial Intelligence in Accounting: Practical Applications was written with a simple goal: to provide accountants with a foundational understanding of AI and its many business and accounting applications. It is meant to serve as a guide for identifying opportunities to implement AI initiatives to increase productivity and profitability. This book will help you answer questions about what AI is and how it is used in the accounting profession today. Offering practical guidance that you can leverage for your organization, this book provides an overview of essential AI concepts and technologies that accountants should know, such as machine learning, deep learning, and natural language processing. It also describes accounting-specific applications of robotic process automation and text mining. Illustrated with case studies and interviews with representatives from global professional services firms, this concise volume makes a significant contribution to examining the intersection of AI and the accounting profession. This innovative book also explores the challenges and ethical considerations of AI. It will be of great interest to accounting practitioners, researchers, educators, and students. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence for Audit, Forensic Accounting, and Valuation Al Naqvi, 2020-08-25 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. |
| artificial intelligence and machine learning in accounting: The Essentials of Machine Learning in Finance and Accounting Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, Mohammed Mohi Uddin, 2021-06-20 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. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence in Accounting and Auditing Mariarita Pierotti, |
| artificial intelligence and machine learning in accounting: Artificial Intelligence in Accounting Cory Ng, John Alarcon, 2020-12-08 Artificial Intelligence in Accounting: Practical Applications was written with a simple goal: to provide accountants with a foundational understanding of AI and its many business and accounting applications. It is meant to serve as a guide for identifying opportunities to implement AI initiatives to increase productivity and profitability. This book will help you answer questions about what AI is and how it is used in the accounting profession today. Offering practical guidance that you can leverage for your organization, this book provides an overview of essential AI concepts and technologies that accountants should know, such as machine learning, deep learning, and natural language processing. It also describes accounting-specific applications of robotic process automation and text mining. Illustrated with case studies and interviews with representatives from global professional services firms, this concise volume makes a significant contribution to examining the intersection of AI and the accounting profession. This innovative book also explores the challenges and ethical considerations of AI. It will be of great interest to accounting practitioners, researchers, educators, and students. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence in Finance Yves Hilpisch, 2020-10-14 The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about |
| artificial intelligence and machine learning in accounting: Artificial Intelligence for Audit, Forensic Accounting, and Valuation Al Naqvi, 2020-07-24 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. |
| artificial intelligence and machine learning in accounting: Machine Learning Applications for Accounting Disclosure and Fraud Detection Papadakis, Stylianos, Garefalakis, Alexandros, Lemonakis, Christos, Chimonaki, Christiana, Zopounidis, Constantin, 2020-10-02 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. |
| artificial intelligence and machine learning in accounting: Machine Learning in Finance Matthew F. Dixon, Igor Halperin, Paul Bilokon, 2020-07-01 This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. |
| artificial intelligence and machine learning in accounting: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| artificial intelligence and machine learning in accounting: Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making Idemudia, Efosa Carroll, 2020-06-05 To date, a plethora of companies and organizations are investing vast amounts of money on the latest technologies. Information technology can be used to improve market share, profits, sales, competitive advantage, and customer/employee satisfaction. Unfortunately, the individuals meant to use these technologies are not well equipped on how to effectively and efficiently use these tools for competitive advantage and decision making. The Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making is a collection of innovative research relevant to the methodologies, theoretical frameworks, and latest empirical research findings in information technology applications, strategic competitive advantage, and decision making. While highlighting topics including agility, knowledge management, and business intelligence, this book is ideally designed for information technology professionals, academics, researchers, managers, executives, and government officials interested in using information technology for strategic competitive advantage and better decision making. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
| artificial intelligence and machine learning in accounting: Advances in Financial Machine Learning Marcos Lopez de Prado, 2018-01-23 Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance. |
| artificial intelligence and machine learning in accounting: Integrative Advisory Services Amy Vetter, 2017-11-20 Stop crunching numbers and start truly serving your clients Integrative Advisory Services is the CPA, accounting professional and bookkeeper's guide to the future. As technology paves the way for increased self-reliance and DIY financial services, much of the traditional data entry tasks of accounting professionals and bookkeepers will be reduced. Yet, nothing can replace the human side of the client-advisor experience and the desire to improve your clients' businesses with financial information. Technology will continue marching on, so accounting professionals must adapt to the changing marketplace to thrive in this new paradigm. This book shows you how to provide the kind of value that technology cannot: human connection. Rather than simply reporting data, today's accounting professionals have an opportunity to take a much more active role in their clients' business by analyzing the story behind the numbers, understanding both operations and finance, and guiding the client toward the outcomes they need. Creating an ongoing relationship throughout the year allows you to be proactive rather than reactive, and help your client's business at a holistic level. Your business owner and CEO clients can get the numbers from the computer too—but, they come to you for personalized advice, explanations, and guidance based on their unique situation and financial needs. This book shows you how to take on more of an advisory role and become a critical component of your client's success. Spend less time crunching numbers and more time advising clients Become an integral part of the client's decision-making process Provide real value by clearly communicating financial data analysis Become the strategic partner your client cannot do without Cloud technology, machine learning, and artificial intelligence are not the death knell for financial advisors; in fact, they're the opposite—they do the number crunching for you, leaving you more time to provide the personal guidance that no computer could. As the financial advisory industry evolves, Integrative Advisory Services is your real-world guide to adapting and thriving. |
| artificial intelligence and machine learning in accounting: The AI Book Ivana Bartoletti, Anne Leslie, Shân M. Millie, 2020-06-29 Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important |
| artificial intelligence and machine learning in accounting: Fintech with Artificial Intelligence, Big Data, and Blockchain Paul Moon Sub Choi, Seth H. Huang, 2021-03-08 This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain—all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths. |
| artificial intelligence and machine learning in accounting: Blockchain, Artificial Intelligence and Financial Services Sean Stein Smith, 2019-11-15 Blockchain technology and artificial intelligence (AI) have the potential to transform how the accounting and financial services industries engage with the business, stakeholder and consumer communities. Presenting a blend of technical analysis with current and future applications, this book provides professionals with an action plan to embrace and move forward with these new technologies in financial and accounting organizations. It is written in a conversational style that is unbiased and objective, replacing jargon and technical details with real world case examples. |
| artificial intelligence and machine learning in accounting: Competing in the Age of AI Marco Iansiti, Karim R. Lakhani, 2020-01-07 a provocative new book — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how collisions between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI. |
| artificial intelligence and machine learning in accounting: Applications of Artificial Intelligence in Business and Finance 5.0 Richa Goel, Vikas Garg, Michela Floris, 2024-12-06 This new book provides a valuable overview of how artificial intelligence (AI) applications are transforming global businesses and financial organizations, looking at the newest artificial intelligence-based solutions for e-commerce, corporate management, finance, banking and trading, and more. Chapters look at using AI and machine learning techniques to forecast and assess financial risks such as liquidity risk, volatility risk, and credit risk. The book also describes the use of natural language processing and text mining paired with machine learning models to assist in guiding sophisticated investors and corporate managers in financial decision making. Other topics include cryptocurrency in emerging markets; the role of artificial intelligence in making a positive impact on sustainable development; the use of fintech for micro, small and medium enterprises; the role of AI i financial education; the application of artificial intelligence in cyber security; and more. |
| artificial intelligence and machine learning in accounting: Advanced Machine Learning Algorithms for Complex Financial Applications Irfan, Mohammad, Elhoseny, Mohamed, Kassim, Salina, Metawa, Noura, 2023-01-09 The advancements in artificial intelligence and machine learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are becoming more complex within the field of finance. Artificial intelligence and machine learning, with their spectacular success accompanied by unprecedented accuracies, have become increasingly important in the finance world. Advanced Machine Learning Algorithms for Complex Financial Applications provides innovative research on the roles of artificial intelligence and machine learning algorithms in financial sectors with special reference to complex financial applications such as financial risk management in big data environments. In addition, the book addresses broad challenges in both theoretical and application aspects of artificial intelligence in the field of finance. Covering essential topics such as secure transactions, financial monitoring, and data modeling, this reference work is crucial for financial specialists, researchers, academicians, scholars, practitioners, instructors, and students. |
| artificial intelligence and machine learning in accounting: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry. |
| artificial intelligence and machine learning in accounting: Human-in-the-Loop Machine Learning Robert Munro, Robert Monarch, 2021-07-20 Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows. |
| artificial intelligence and machine learning in accounting: Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) Cheng Few Lee, John C Lee, 2020-07-30 This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience. |
| artificial intelligence and machine learning in accounting: Handbook of Research on Innovative Management Using AI in Industry 5.0 Garg, Vikas, Goel, Richa, 2021-11-19 There is no industry left where artificial intelligence is not used in some capacity. The application of this technology has already stretched across a multitude of domains including law and policy; it will soon permeate areas beyond anyone’s imagination. Technology giants such as Google, Apple, and Facebook are already investing their money, effort, and time toward integrating artificial intelligence. As this technology continues to develop and expand, it is critical for everyone to understand the various applications of artificial intelligence and its full potential. The Handbook of Research on Innovative Management Using AI in Industry 5.0 uncovers new and innovative features of artificial intelligence and how it can help in raising economic efficiency at both micro and macro levels and provides a deeper understanding of the relevant aspects of artificial intelligence impacting efficacy for better output. Covering topics such as consumer behavior, information technology, and personalized banking, it is an ideal resource for researchers, academicians, policymakers, business professionals, companies, and students. |
| artificial intelligence and machine learning in accounting: Big Data Analytics for Cyber-Physical System in Smart City Mohammed Atiquzzaman, Neil Yen, Zheng Xu, 2020-12-17 This book gathers a selection of peer-reviewed papers presented at the second Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020) conference, held in Shanghai, China, on 28–29 December 2020. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence in Banking Introbooks, 2020-04-07 In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, In a world focused on using AI in new ways, we're focused on using it wisely and responsibly. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence and Machine Learning for EDGE Computing Rajiv Pandey, Sunil Kumar Khatri, Neeraj Kumar Singh, Parul Verma, 2022-04-26 Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. - Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing - Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers - Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints |
| artificial intelligence and machine learning in accounting: The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry Stephanie K. Ashenden, 2021-04-23 The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient's life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. - Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research - Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved - Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide |
| artificial intelligence and machine learning in accounting: The Economics of Artificial Intelligence Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker, 2024-03-05 A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system. |
| artificial intelligence and machine learning in accounting: The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success Allam Hamdan, Aboul Ella Hassanien, Anjum Razzaque, Bahaaeddin Alareeni, 2021-04-11 This book focuses on the implementation of AI for growing business, and the book includes research articles and expository papers on the applications of AI on decision-making, health care, smart universities, public sector and digital government, FinTech, and RegTech. Artificial Intelligence (AI) is a vital and a fundamental driver for the Fourth Industrial Revolution (FIR). Its influence is observed at homes, in the businesses and in the public spaces. The embodied best of AI reflects robots which drive our cars, stock our warehouses, monitor our behaviors and warn us of our health, and care for our young children. Some researchers also discussed the role of AI in the current COVID-19 pandemic, whether in the health sector, education, and others. On all of these, the researchers discussed the impact of AI on decision-making in those vital sectors of the economy. |
| artificial intelligence and machine learning in accounting: Machine Learning for Asset Management Emmanuel Jurczenko, 2020-10-06 This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management. |
| artificial intelligence and machine learning in accounting: Machine Learning and Artificial Intelligence in Marketing and Sales Niladri Syam, Rajeeve Kaul, 2021-03-10 Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer programming. |
| artificial intelligence and machine learning in accounting: AI and Financial Markets Shigeyuki Hamori, Tetsuya Takiguchi, 2020-07-01 Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets. |
| artificial intelligence and machine learning in accounting: 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) IEEE Staff, 2021-04-09 The 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP 2021) is the IEEE Consumer Electronics Society s annual conference that will take place in conjunction with CES ICSP 2021 will bring together top professionals from industry, government, and academia from around the world ICSP 2021 includes invited talks, oral presentations and poster presentations of refereed papers We invite submissions of papers and abstracts on all topics related to Intelligent Computing and Signal Processing This conference offers good opportunities for the delegates to exchange new ideas, and to establish research and or business links, as well as to build global partnership for potential collaboration The conference will provide networking opportunities for participants to share ideas, designs, and experiences on the state of the art and future direction of Intelligent Computing and Signal Processing |
| artificial intelligence and machine learning in accounting: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| artificial intelligence and machine learning in accounting: Empirical Asset Pricing Wayne Ferson, 2019-03-12 An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence for Asset Management and Investment Al Naqvi, 2021-02-09 Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector 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 finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations. |
| artificial intelligence and machine learning in accounting: Artificial Intelligence and the Changing Nature of Corporations Tankiso Moloi, Tshilidzi Marwala, 2021 This book explains how various forms of artificial intelligence, namely machine learning, natural language processing, and robotic process automation, could provide a source of competitive advantage to firms deploying them compared to those firms that would not have deployed these technologies. The advantages of machine learning, natural language processing, and robotic process automation in strategy formulation and strategy implementation are explored. The book illustrates the potential sources of advantage for the strategy formulation and strategy implementation processes, which can be derived from the deployment of each form of artificial intelligence. |
| artificial intelligence and machine learning in accounting: Applications of Artificial Intelligence in Business and Finance Vikas Garg, Shalini Aggarwal, Pooja Tiwari, Prasenjit Chatterjee, 2021-12-23 As transactions and other business functions move online and grow more popular every year, the finance and banking industries face increasingly complex data management and identity theft and fraud issues. AI can bring many financial and business functions to the next level, as systems using deep learning technologies are able to analyze patterns and spot suspicious behavior and potential fraud. In this volume, the focus is on the application of artificial intelligence in finance, business, and related areas. The book presents a selection of chapters presenting cutting-edge research on current business practices in finance and management. Topics cover the use of AI in e-commerce systems, financial services, fraud prevention, identifying loan-eligible customers, online business, Facebook social commerce, insurance industry, online marketing, and more. |
| artificial intelligence and machine learning in accounting: The End of Accounting and the Path Forward for Investors and Managers Baruch Lev, Feng Gu, 2016-06-14 An innovative new valuation framework with truly useful economic indicators The End of Accounting and the Path Forward for Investors and Managers shows how the ubiquitous financial reports have become useless in capital market decisions and lays out an actionable alternative. Based on a comprehensive, large-sample empirical analysis, this book reports financial documents' continuous deterioration in relevance to investors' decisions. An enlightening discussion details the reasons why accounting is losing relevance in today's market, backed by numerous examples with real-world impact. Beyond simply identifying the problem, this report offers a solution—the Value Creation Report—and demonstrates its utility in key industries. New indicators focus on strategy and execution to identify and evaluate a company's true value-creating resources for a more up-to-date approach to critical investment decision-making. While entire industries have come to rely on financial reports for vital information, these documents are flawed and insufficient when it comes to the way investors and lenders work in the current economic climate. This book demonstrates an alternative, giving you a new framework for more informed decision making. Discover a new, comprehensive system of economic indicators Focus on strategic, value-creating resources in company valuation Learn how traditional financial documents are quickly losing their utility Find a path forward with actionable, up-to-date information Major corporate decisions, such as restructuring and M&A, are predicated on financial indicators of profitability and asset/liabilities values. These documents move mountains, so what happens if they're based on faulty indicators that fail to show the true value of the company? The End of Accounting and the Path Forward for Investors and Managers shows you the reality and offers a new blueprint for more accurate valuation. |
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
artificial - Wiktionary, the free dictionary
6 days ago · artificial (comparative more artificial, superlative most artificial) Man-made; made by humans; of artifice. The flowers were artificial, and he thought them rather tacky. An artificial …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
Artificial Intelligence Is Not Intelligent - The Atlantic
Jun 6, 2025 · The good news is that nothing about this is inevitable: According to a study released in April by the Pew Research Center, although 56 percent of “AI experts” think artificial …
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
artificial - Wiktionary, the free dictionary
6 days ago · artificial (comparative more artificial, superlative most artificial) Man-made; made by humans; of artifice. The flowers were artificial, and he thought them rather tacky. An artificial …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
Artificial Intelligence Is Not Intelligent - The Atlantic
Jun 6, 2025 · The good news is that nothing about this is inevitable: According to a study released in April by the Pew Research Center, although 56 percent of “AI experts” think artificial …
