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Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning (Wiley and SAS Business Series)

Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning (Wiley and SAS Business Series)

Risk Modeling: Practical Applications of Artificial Intelligence, Machine
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Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning (Wiley and SAS Business Series) Hardback - 2022

by Roberts, Terisa

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Wiley, 2022-09-27. 1. hardcover. Used: Good. 6.20x0.90x9.10. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning (Wiley and SAS Business Series)
  • Author Roberts, Terisa
  • Binding Hardback
  • Edition 1
  • Condition Used: Good
  • Pages 208
  • Volumes 1
  • Language ENG
  • Publisher Wiley
  • Publication date 2022-09-27
  • Features Index
  • Bookseller's Inventory # SONG1119824931
  • ISBN 9781119824930 / 1119824931
  • Weight 0.83 lbs (0.38 kg)
  • Dimensions 9.18 x 6.3 x 0.82 in (23.32 x 16.00 x 2.08 cm)
  • Size 6.20x0.90x9.10
  • Category Business / Economics / Finance
  • Library of Congress subjects Risk management, Artificial intelligence
  • Library of Congress Catalogue Number 2022032486
  • Dewey Decimal Code 658.155
  • Quantity available 1

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From the publisher

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.

From the jacket flap

In Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning, 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.

You'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.

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, Risk Modeling 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.

Perfect for C-suite executives, risk managers, and other business leaders, Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning 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.

About the author

TERISA ROBERTS, PHD, 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.

STEPHEN J. TONNA, PHD, 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.

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