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Following rapid technological advancements that have taken place throughout the late twentieth and early twenty-first centuries, this intriguing book provides a dynamic agenda for the study of artificial intelligence (AI) within finance. Through an in-depth consideration of the use of AI, it utilizes case study examples to investigate AI’s effectiveness within investment and banking.
This book describes the latest advances in intelligent techniques such as fuzzy logic, neural networks, and optimization algorithms, and their relevance in building intelligent information systems in combination with applied mathematics. The authors also outline the applications of these systems in areas like intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems. By sharing fresh ideas and identifying new targets/problems it offers young researchers and students new directions for their future research. The book is intended for readers from mathematics and computer science, in particular professors and students working on theory and applications of intelligent systems for real-world applications.
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
This book starts from the application scenarios of artificial financial intelligence regulation, commercial banking, wealth management and payments, etc., and makes a detailed study of the main scenarios of the application of China's artificial intelligence in the financial field, and also analysis specific application cases of China.With the popularization of smart phones and the rapid development of e-commerce, mobile payment, big data and other technologies are in the ascendant in China in recent years. In particular, artificial intelligence technologies in the form of facial, speech and semantic recognition are showing preliminary advantages in the field of FinTech, and the future era of Intelligent Finance has quietly come. The Chinese government has clearly put forward "China should rely on a robust cycle of domestic demand and innovation as the main driver of the economy while maintaining foreign markets and investors as a second engine of growth", science and technology innovation is the basic motivation of economic and social cycle, to implement the " dual circulation strategy ", it is necessary to understand the key role of scientific and technological innovation in financial innovation services, and improve financial services must be driven by science and technology. There is a natural relationship between artificial intelligence and financial services, because financial services are credit and information intermediaries, and data is the most critical for finance, while artificial intelligence has a super ability in dealing with complex data. At present, many Chinese Banks have applied artificial intelligence to their daily operations and management, such as accurate customer identification, enhanced process tracking, intelligent marketing, and product process transformation, so as to simplify financial service processes and shorten service cycles. In General, this book both pays attention to practical application and theoretical, which is a useful reference book in theoretical research and practical work, and also helps readers to understand the application of intelligent finance in China.
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 for Capital Market throws light on the application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book. Features: Showcases artificial intelligence in finance service industry Explains credit and risk analysis Elaborates on cryptocurrencies and blockchain technology Focuses on the optimal choice of asset pricing model Introduces testing of market efficiency and forecasting in the Indian stock market This book serves as a reference book for academicians, industry professionals, traders, finance managers and stock brokers. It may also be used as textbook for graduate level courses in financial services and financial analytics.
As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.
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
This important book presents new and original work at the frontiers of economics, namely the interface between artificial intelligence (AI) and neoclassical economics. Artificial Intelligence and Economic Analysis focuses on three quite distinct lines of AI orientated research in economics: applications intended to extend neoclassical theory, applications intended to undermine neoclassical theory and applications which ignore neoclassical theory in the quest for new modelling techniques and fields of analysis. The contributors - all of whom are well established in the field - do not simply report established results but seek to identify those areas where the science of artificial intelligence could enrich standard economic analysis. It includes material from mainstream economists who are willing to express their own views about the limits of mainstream economic modelling and AI based economic modelling. The book makes an important contribution to a new and exciting area of economics which holds much hope for the future.
Artificial intelligence (AI) has the potential to significantly improve efficiency, reduce costs, and increase the speed and accuracy of financial decision-making, making it an increasingly important tool for financial professionals. One way that AI can improve efficiency in finance is by automating tasks and processes that are time-consuming and repetitive for humans. For example, AI algorithms can be used to analyze and process large amounts of data, such as financial statements and market data, in a fraction of the time that it would take a human to do so. This can allow financial professionals to focus on higher-value tasks, such as interpreting data and making strategic decisions, rather than being bogged down by mundane tasks. AI can also reduce costs in finance by increasing automation and eliminating the need for certain tasks to be performed manually. This can result in cost savings for financial institutions, which can then be passed on to customers in the form of lower fees or better services. AI can be used to identify unusual patterns of activity that may indicate fraudulent behavior. This can help financial institutions reduce losses from fraud and improve customer security. AI-powered chatbots and virtual assistants can help financial institutions provide faster, more efficient customer service, particularly when it comes to answering common questions and handling routine tasks. Some financial institutions are using AI to analyze market data and make trades in real-time. AI-powered trading algorithms can potentially make faster and more accurate trading decisions than humans. In terms of speed and accuracy, AI algorithms can analyze data and make decisions much faster than humans, and can do so with a high degree of accuracy. This can be particularly useful in fast-moving financial markets, where quick and accurate decision-making can be the difference between success and failure. This book highlights how AI in finance can improve efficiency, reduce costs, and increase the speed and accuracy of financial decision-making. Moreover, the book also focuses on how to ensure the responsible and ethical use of AI in finance. This book is a valuable resource for students, scholars, academicians, researchers, professionals, executives, government agencies, and policymakers interested in exploring the role of artificial intelligence (AI) in finance. Its goal is to provide a comprehensive overview of the latest research and knowledge in this area, and to stimulate further inquiry and exploration.