Disrupting Finance - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Disrupting Finance write by Theo Lynn. This book was released on 2018-12-06. Disrupting Finance available in PDF, EPUB and Kindle. 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 for Risk Management
Artificial Intelligence for Risk Management - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence for Risk Management write by Archie Addo. This book was released on 2020-03-13. Artificial Intelligence for Risk Management available in PDF, EPUB and Kindle. Artificial Intelligence (AI) for Risk Management is about using AI to manage risk in the corporate environment. The content of this work focuses on concepts, principles, and practical applications that are relevant to the corporate and technology environments. The authors introduce AI and discuss the different types, capabilities, and purposes–including challenges. With AI also comes risk. This book defines risk, provides examples, and includes information on the risk-management process. Having a solid knowledge base for an AI project is key and this book will help readers define the knowledge base needed for an AI project by developing and identifying objectives of the risk-knowledge base and knowledge acquisition for risk. This book will help you become a contributor on an AI team and learn how to tell a compelling story with AI to drive business action on risk.
Machine Learning for Financial Risk Management with Python
Machine Learning for Financial Risk Management with Python - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Financial Risk Management with Python write by Abdullah Karasan. This book was released on 2021-12-07. Machine Learning for Financial Risk Management with Python available in PDF, EPUB and Kindle. Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance write by El Bachir Boukherouaa. This book was released on 2021-10-22. Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance available in PDF, EPUB and Kindle. 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.
Risk Modeling
Risk Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Risk Modeling write by Terisa Roberts. This book was released on 2022-09-20. Risk Modeling available in PDF, EPUB and Kindle. 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.
