{"product_id":"risk-modeling-practical-applications-of-artificial-intelligence-machine-learning-and-deep-learning-hardcover","title":"Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning - Hardcover","description":"\u003cp\u003eby \u003cb\u003eTerisa Roberts\u003c\/b\u003e (Author), \u003cb\u003eStephen J. Tonna\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eAuthors 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: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eHighlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk\u003c\/li\u003e \u003cli\u003eDiscusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques\u003c\/li\u003e \u003cli\u003eCovers the basic principles and nuances of feature engineering and common machine learning algorithms\u003c\/li\u003e \u003cli\u003eIllustrates 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\u003c\/li\u003e \u003cli\u003eExplains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e In \u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e, distinguished risk and analytics professionals Terisa Roberts and Stephen J. Tonna deliver an innovative and insightful exploration of the latest artificial intelligence technologies used to forecast and evaluate financial risks. The authors offer up-to-date information on how to apply current modeling techniques in risk management, as well as new opportunities and challenges associated with the implementation of artificial intelligence (AI) and machine learning (ML) in the risk management process. \u003c\/p\u003e \u003cp\u003eYou'll learn the strengths and weaknesses of AI and ML where they're applied to everyday risk management problems or to once-in-a-lifetime \"black swan\" events, like global pandemics or climate shocks. The authors clarify common misconceptions about AI and ML and offer step-by-step guidance to using the modern technologies within your organization's existing risk management framework. \u003c\/p\u003e\u003cp\u003e The book provides practical tools for assessing bias and the interpretability of ML models. It also covers the basic principles of feature engineering and the most commonly used ML algorithms. The authors discuss how risk modeling incorporates AI and ML to rapidly process complicated data and fills the gaps currently existing in the end-to- end risk modeling lifecycle. Finally, \u003ci\u003eRisk Modeling\u003c\/i\u003e explains how proprietary software and open-source languages can be combined to deliver the best of both worlds for risk models and for risk practitioners. \u003c\/p\u003e\u003cp\u003e Perfect for C-suite executives, risk managers, and other business leaders, \u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e is also an indispensable resource for compliance officers and managers, as well as anyone else who seeks to apply the latest AI and ML learning techniques to solve or mitigate quantitative risk problems.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for Risk Modeling \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"This book is highly accessible and directed at practitioners interested in the application of AI and ML in the financial services industry. I first met Terisa over twenty years ago and have marveled at her growth in the analytics space and ability to communicate regarding complex topics.\"\u003cbr\u003e\u003cb\u003e --RAYMOND ANDERSON, \u003c\/b\u003e Rayan Risk Analytics\u003c\/p\u003e \u003cp\u003e\"This comprehensive text answers all the critical questions bankers have been asking around using AI and ML for risk modeling for years. It should be part of every risk modeler's library.\" \u003cbr\u003e\u003cb\u003e --NAEEM SIDDIQI, \u003c\/b\u003e Senior Risk Advisor, SAS Institute\u003c\/p\u003e \u003cp\u003e\"An ideal read for managers or senior managers in any financial institution. Roberts and Tonna's writing is clear, direct, accurate, and uses exactly the right level of technicality to get to each point.\"\u003cbr\u003e\u003cb\u003e --ALAN FORREST, \u003c\/b\u003e Advisory Senior Manager, Model Risk Oversight\u003cbr\u003e\u003cbr\u003e\"Machine Learning is disrupting the world of model and data governance. Roberts and Tonna succinctly describe how forward-looking organizations will pragmatically use these approaches to responsibly drive profits and gain a competitive advantage.\"\u003cbr\u003e\u003cb\u003e--DAVID ASERMELY, \u003c\/b\u003eGlobal Lead, Model Risk Management\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eTERISA ROBERTS, PHD, \u003c\/b\u003e is Global Solution Lead for Risk Modeling and Decisioning at SAS. She has nearly twenty years of experience in quantitative risk management and advanced analytics. She regularly advises banks and regulators around the world on industry best practices in AI, automation, and digitalization related to risk modeling and decisioning. \u003c\/p\u003e \u003cp\u003e\u003cb\u003eSTEPHEN J. TONNA, PHD, \u003c\/b\u003e is a Senior Banking Solutions Advisor at SAS. He is a member of the SAS Risk Finance Advisory team for SAS Risk Research and Quantitative Solutions (RQS) in Asia Pacific. He received his doctorate in genetics, mathematics, and statistics from the University of Melbourne and research fellowship from the Brigham and Women's Hospital and Harvard Medical School.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 208\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.82 x 9.18 x 6.3 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 27, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51765786968352,"sku":"9781119824930","price":49.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/83b875857df47105e398586c24d2b5a2.webp?v=1780280026","url":"https:\/\/ebocreations.com\/products\/risk-modeling-practical-applications-of-artificial-intelligence-machine-learning-and-deep-learning-hardcover","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}