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Essay On Big Data And Machine Learning In Finance
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Book Synopsis Essay on Big Data and Machine Learning in Finance by : Gunsu Son
Download or read book Essay on Big Data and Machine Learning in Finance written by Gunsu Son and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite structural differences between the options and stock markets, few studies have discussed the behavior and impact of high-frequency traders (HFTs) in the options market. Options exchanges identify high-frequency/algorithmic traders as Professional Customers (PCs). In this study, we use granular data that identifies trades by customers, PCs, and Market Makers (MMs). We find that PCs mainly trade as a counterparty to customers, similar to MMs. However, the liquidity provision by PCs leads to order flow toxicity: PCs use a "cream skimming" strategy that imposes adverse selection costs on MMs. PCs mainly trade with uninformed customers, most likely leveraging their speed and algorithmic advantage. PCs provide less liquidity when the market and stock volatility are high. Customer call option trades made with PCs have one-tenth of price impact and no return or volatility predictability, while there is significant price impact in addition to return and volatility predictability when executed against MMs during the next 30 minutes. Our finding on HFTs' non-arbitrage channel of order flow toxicity is new and suggests that the role of HFTs should be better understood in the context of the options market structure.
Book Synopsis Big Data and Artificial Intelligence in Digital Finance by : John Soldatos
Download or read book Big Data and Artificial Intelligence in Digital Finance written by John Soldatos and published by Springer Nature. This book was released on 2022 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance. Introduces the latest advances in Big Data and AI in Digital Finance that enable scalable, effective, and real-time analytics; Explains the merits of Blockchain technology in digital finance, including applications beyond the blockbuster cryptocurrencies; Presents practical applications of cutting edge digital technologies in the digital finance sector; Illustrates the regulatory environment of the financial sector and presents technical solutions that boost compliance to applicable regulations; This book is open access, which means that you have free and unlimited access.
Book Synopsis Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by : El Bachir Boukherouaa
Download or read book Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat
Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Book Synopsis Financial Data Analytics with Machine Learning, Optimization and Statistics by : Yongzhao Chen
Download or read book Financial Data Analytics with Machine Learning, Optimization and Statistics written by Yongzhao Chen and published by John Wiley & Sons. This book was released on 2024-11-19 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
Book Synopsis The Essentials of Machine Learning in Finance and Accounting by : Mohammad Zoynul Abedin
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 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: • A useful guide to financial product modeling and to minimizing business risk and uncertainty • Looks at wide range of financial assets and markets and correlates them with enterprises’ profitability • Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets • Real world applicable examples to further understanding
Book Synopsis Big Data Analytics and Machine Learning in Finance by : Miriam Wastl
Download or read book Big Data Analytics and Machine Learning in Finance written by Miriam Wastl and published by GRIN Verlag. This book was released on 2019-07-15 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studienarbeit aus dem Jahr 2018 im Fachbereich BWL - Unternehmensführung, Management, Organisation, Note: 1,3, Universität Regensburg, Sprache: Deutsch, Abstract: Im Rahmen dieser Arbeit zum Thema Big Data Analytics werden verschiedene Anwendungsbereiche des Machine Learnings aufgezeigt. Beginnend mit einer kurzen Darlegung der wesentlichen Konzepte von Big Data und des MachineLearnings werden anschließend Applikationen dessen in den Bereichen Handelsstrategien, Kreditscoring sowie Betrugserkennung vorgestellt. Außerdem erfolgt eine Anwendung der logistischen Regression sowie eines Entscheidungsbaums um ein binäres Model zur Klassifikation betrügerischer Daten zu konstruieren. Es konnte festgestellt werden, dass bei großen Datenmengen Machine Learning Methoden zur Analyse derer essentiell sind und großen Nutzen hinsichtlich der Erkennung von Mustern in den Daten stiften.
Book Synopsis Machine Learning and Data Sciences for Financial Markets by : Agostino Capponi
Download or read book Machine Learning and Data Sciences for Financial Markets written by Agostino Capponi and published by Cambridge University Press. This book was released on 2023-04-30 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado
Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular 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.
Book Synopsis Machine Learning for Finance by : Saurav Singla
Download or read book Machine Learning for Finance written by Saurav Singla and published by BPB Publications. This book was released on 2021-01-05 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the essentials of Machine Learning and its impact in financial sector KEY FEATURESÊ _Explore the spectrum of machine learning and its usage. _Understand the NLP and Computer Vision and their use cases. _Understand the Neural Network, CNN, RNN and their applications. _ÊUnderstand the Reinforcement Learning and their applications. _Learn the rising application of Machine Learning in the Finance sector. Ê_Exposure to data mining, data visualization and data analytics. DESCRIPTION The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation.Ê Ê The book demonstrates how to solve some of the most common issues in the financial industry.Ê The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Nave Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms. Ê Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. WHAT WILL YOU LEARN _ Ê Ê Ê You will grasp the most relevant techniques of Machine Learning for everyday use. _ Ê Ê Ê You will be confident in building and implementing ML algorithms. _ Ê Ê Ê Familiarize the adoption of Machine Learning for your business need. _ Ê Ê Ê Discover more advanced concepts applied in banking and other sectors today. _ Ê Ê Ê Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. WHO THIS BOOK IS FORÊ Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain. TABLE OF CONTENTS 1.Introduction 2.Naive Bayes, Normal Distribution and Automatic Clustering Processes 3.Machine Learning for Data Structuring 4.Parsing Data Using NLP 5.Computer Vision 6.Neural Network, GBM and Gradient Descent 7.Sequence Modeling 8.Reinforcement Learning For Financial Markets 9.Finance Use Cases 10.Impact of Machine Learning on Fintech 11.Machine Learning in Finance 12.eKYC and Anti-Fraud Policy 13.Uses of Data Mining and Data Visualization 14.Advantages and Disadvantages of Machine Learning 15.Applications of Machine Learning in Other Industries 16.Ethical considerations in Artificial Intelligence 17.Artificial Intelligence in Banking 18.Common Machine Learning Algorithms 19.Frequently Asked Questions
Book Synopsis Big Data and Machine Learning in Quantitative Investment by : Tony Guida
Download or read book Big Data and Machine Learning in Quantitative Investment written by Tony Guida and published by John Wiley & Sons. This book was released on 2019-03-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Book Synopsis Three Essays on Big Data in International Finance by : Ziqi Zang
Download or read book Three Essays on Big Data in International Finance written by Ziqi Zang and published by . This book was released on 2019 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents an introduction to big data that can potentially be used in nowcasting key macroeconomic variables for advanced economies. It also explores the forecastability of big data in short-term exchange rate forecasting. Finally, it draws on evidence from a sentiment analysis of Article IV Consultations over the period of 2012 to 2018 and examines the development of member countries' perceptions of IMF policy advice. Chapter 1 uses big data from Google search data to form better nowcasts of macroeconomic variables. My empirical strategy contributes to the macroeconomic nowcasting literature on three fronts. First, I take a number of steps to identify the most comprehensive set of relevant search queries that capture people's search behavior in relation to each monetary policy variable, such as the unemployment rate and inflation. Second, I consider regularization and dimension reduction methods to handle the underlying high-dimensional regressor space with highly correlated covariates. Third, I evaluate both average point forecasts and conditional point forecasts against benchmark models with DMW test and CSPA test, respectively. According to the test statistics, I find that Google search data offer significant improvements in nowcasting macroeconomic variables both unconditionally and conditionally. Chapter 2 examines the short-term forecastability of exchange rates using machine learning models in a rich data environment. I investigate the performance of different machine learning models, such as variable selection models, dynamic factor model, and decision regression trees in obtaining accurate forecasts of three currency pairs (U.S./U.K., Japan/U.S. and U.S./Australia). I consider three types of forecasts: point forecasts, unconditional weighted directional forecasts and conditional weighted directional forecasts. According to the DMW test, out-of-sample forecasts of every currency rejects the null hypothesis of equal forecasting errors with the random walk with at least one machine learning model. Furthermore, the conditional weighted directional forecasts allow us to know when exactly our models are more profitable than the random walk with zero profit. And it turns out that our weighted directional forecasts are significantly positive especially on the tails of the conditioning variable distribution. Chapter 3 constructs multi-aspect policy sentiment measurements to interpret authorities' tones in response to specific policy advice in IMF Article IV Consultations. Specifically, we use a topic-based sentiment analysis approach that entails the application of a latent Dirichlet allocation (LDA) model as well as sentiment prediction machine learning models. Therefore, we are able to provide the stylized facts that provide useful input for assessing the impact of Fund advice on macroeconomic development of member countries.
Book Synopsis Artificial Intelligence and Big Data for Financial Risk Management by : Noura Metawa
Download or read book Artificial Intelligence and Big Data for Financial Risk Management written by Noura Metawa and published by Taylor & Francis. This book was released on 2022-08-26 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment.
Book Synopsis Advanced Machine Learning Algorithms for Complex Financial Applications by : Irfan, Mohammad
Download or read book Advanced Machine Learning Algorithms for Complex Financial Applications written by Irfan, Mohammad and published by IGI Global. This book was released on 2023-01-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Novel Financial Applications of Machine Learning and Deep Learning by : Mohammad Zoynul Abedin
Download or read book Novel Financial Applications of Machine Learning and Deep Learning written by Mohammad Zoynul Abedin and published by Springer Nature. This book was released on 2023-03-01 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Book Synopsis Artificial Intelligence in Financial Services and Banking Industry by : Dr. V.V.L.N. Sastry
Download or read book Artificial Intelligence in Financial Services and Banking Industry written by Dr. V.V.L.N. Sastry and published by Idea Publishing. This book was released on 2020-03-20 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last couple of years, the finance and banking sectors have increasingly deployed and implemented Artificial Intelligence (AI) technologies. AI and machine learning are being rapidly adopted for a range of applications for front-end and back end processes to both business and financial management operations. Thus, it is quite significant to consider the financial stability repercussions of such uses. Since AI is relatively new, the data on the usage is largely unavailable, any analysis may be necessarily considered Preliminary1 . Some of the current and potential use cases of AI and machine learning in the finance sector include the following. Institutions use AI and machine learning methods to optimize scarce capital, back-test models, and analyze the market impact of trading large positions. Financial institutions and vendors use AI and machine learning techniques to evaluate credit quality for market and price insurance contracts, and to automate client interaction. Brokers, hedge funds, and other firms are using AI and machine learning to find pointers for higher (and uncorrelated) returns to optimize trading execution. Private and public sector institutions use these technologies for data quality assessment, surveillance, regulatory compliance, and fraud detection. This book seeks to map the use of AI in current state of affairs in the banking and financial sector. By doing so, it explores: The present uses of AI in banking and finance and its narrative across the globe.
Book Synopsis Fintech with Artificial Intelligence, Big Data, and Blockchain by : Paul Moon Sub Choi
Download or read book Fintech with Artificial Intelligence, Big Data, and Blockchain written by Paul Moon Sub Choi and published by Springer Nature. This book was released on 2021-03-08 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
