Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Three Essays On The Application Of Machine Learning For Risk Governance In Financial Institutions
Download Three Essays On The Application Of Machine Learning For Risk Governance In Financial Institutions full books in PDF, epub, and Kindle. Read online Three Essays On The Application Of Machine Learning For Risk Governance In Financial Institutions ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Three Essays on the Application of Machine Learning for Risk Governance in Financial Institutions by : Abena Fosua Owusu
Download or read book Three Essays on the Application of Machine Learning for Risk Governance in Financial Institutions written by Abena Fosua Owusu and published by . This book was released on 2020 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Risk Modeling written by Terisa Roberts and published by John Wiley & Sons. This book was released on 2022-09-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
Book Synopsis Machine Learning in Banking Risk Management by : Mourine Atsien
Download or read book Machine Learning in Banking Risk Management written by Mourine Atsien and published by GRIN Verlag. This book was released on 2023-02-20 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essay from the year 2022 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: A, , course: Business and technology, language: English, abstract: Technological applications are playing a more influential role in management in the contemporary business environment. Machine learning, artificial intelligence, and other algorithmic applications are some of the most common influencers in business applications. They present numerous solutions to business management problems, including banking risk management. In the last decade, risk management has gained greater prominence in financial services. In the past, banks focused on the detection, measuring, and reporting of risks. However, they are now leveraging on machine learning for greater accuracy and efficacy in risk management. As such, this paper explored different ways that machine learning applies in banking risk management. To achieve the objective of this study, the researcher conducted a comprehensive literature review on the topic of machine learning in banking risk management. The researcher found considerable industry and academic research focusing on developments in the financial services industry, especially in relation to risk management. It reviewed the literature, analysing and evaluating various risk management machine-learning techniques. It identified risk management problem areas and explored various ways of addressing them. The review showed that machine learning learning in risk management in financial services sector was still under-researched. While there were many studies on credit risks, other risks such as liquidity risks, market risks, and operational risks saw minimal attention. Nevertheless, machine learning applications were found to have the potential to develop more effective risk management models. Machine learning is leveraged on different data types to predict potential events with greater accuracy and estimate losses associated with different risk types. In addition, the machine learning techniques in risk management were found to provide better and more accurate results than traditional statistical models. Though machine learning suggests improving banking risk management, there are some areas that need further study. For instance, the paper suggested in-depth studies on machine learning models for different types of banking risks.
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 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 Machine Learning for Risk Calculations by : Ignacio Ruiz
Download or read book Machine Learning for Risk Calculations written by Ignacio Ruiz and published by John Wiley & Sons. This book was released on 2021-12-28 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner’s View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions. This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You’ll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you’ll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used. Review the fundamentals of deep learning and Chebyshev tensors Discover pioneering algorithmic techniques that can create new opportunities in complex risk calculation Learn how to apply the solutions to a wide range of real-life risk calculations. Download sample code used in the book, so you can follow along and experiment with your own calculations Realize improved risk management whilst overcoming the burden of limited computational power Quants, IT professionals, and financial risk managers will benefit from this practitioner-oriented approach to state-of-the-art risk calculation.
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 Three Essays on High Frequency Financial Data and Their Use for Risk Management by : Maria Pacurar
Download or read book Three Essays on High Frequency Financial Data and Their Use for Risk Management written by Maria Pacurar and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Three Essays on the Application of Machine Learning Methods in Economics by : Abdelaziz Lawani
Download or read book Three Essays on the Application of Machine Learning Methods in Economics written by Abdelaziz Lawani and published by . This book was released on 2018 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Three Essays on Machine Learning in Empirical Finance by : Jinhua Wang
Download or read book Three Essays on Machine Learning in Empirical Finance written by Jinhua Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Machine Learning and Price Impact in Institutional Finance by : Zihan Lin (Researcher in machine learning)
Download or read book Essays on Machine Learning and Price Impact in Institutional Finance written by Zihan Lin (Researcher in machine learning) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Institutional investors play crucial roles in financial markets. First, they delegate investment for individual investors. We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, and the returns of predictive long-short portfolios are higher following a period of high sentiment. Second, institutional investors provide liquidity to investor demand. We hypothesize and provide evidence that prices are more inelastic when demand is less diversifiable. We decompose order-flow imbalances into components with varying degrees of diversifiability and estimate their price impacts. Our findings are consistent with weaker liquidity provision at less diversifiable levels.
Download or read book Journal of Economic Literature written by and published by . This book was released on 1998 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence in Banking by : Introbooks
Download or read book Artificial Intelligence in Banking written by Introbooks and published by . This book was released on 2020-04-07 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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."
Download or read book FinTech written by Ross P. Buckley and published by Cambridge University Press. This book was released on 2023-11-23 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this comprehensive, accessible work, Ross P. Buckley, Douglas W. Arner, and Dirk A. Zetzsche offer an ideal reference for anyone seeking to understand the technological transformation of finance and the role of regulation: the world of FinTech. They consider FinTech technologies including artificial intelligence, blockchain, BigData, cloud computing, cryptocurrencies, central bank digital currencies, and distributed ledger technology, and provide a unique perspective on FinTech as an interactive system involving finance, technology, law, and regulation. Starting with an evolutionary perspective, the authors then consider the major technologies transforming finance, arguing for approaches to balance the risks and challenges of innovation. They address the central role of infrastructure in digital financial transformation, highlighting lessons from China, India, and the EU, as well as the impact of pandemics and other sustainability crises, while considering the risks generated by FinTech. They conclude by offering forward-looking regulatory strategies to address the challenges facing our world today.
Book Synopsis Emerging Methods in Predictive Analytics: Risk Management and Decision-Making by : Hsu, William H.
Download or read book Emerging Methods in Predictive Analytics: Risk Management and Decision-Making written by Hsu, William H. and published by IGI Global. This book was released on 2014-01-31 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2009 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Human + Machine, Updated and Expanded by : Paul R. Daugherty
Download or read book Human + Machine, Updated and Expanded written by Paul R. Daugherty and published by Harvard Business Press. This book was released on 2024-09-10 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI—including generative AI—is radically transforming business. Are you ready? Accenture technology leaders Paul Daugherty and Jim Wilson provide crucial insights and advice to help you meet the challenge. Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now—in software that senses what we need, supply chains that "think" in real time, and now generative AI that is radically reshaping work and productivity. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In this updated and expanded edition of Human + Machine—including a new chapter on gen AI—Accenture technology leaders Paul Daugherty and Jim Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization, whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly—or completely reimagine them. Based on the authors' experience and research with fifteen hundred organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability and what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader's guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in the new age of AI.
