Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning - Rilegato

Roberts, Terisa; Tonna, Stephen J.

Rilegato

ISBN 10: 1119824931 ISBN 13: 9781119824930

Casa editrice: John Wiley & Sons Inc, 2022

Riassunto

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.

Su questo libro

Informazioni sull?autore

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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Praise for Risk Modeling

“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.”
—RAYMOND ANDERSON, Rayan Risk Analytics

“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.”
—NAEEM SIDDIQI, Senior Risk Advisor, SAS Institute

“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.”
—ALAN FORREST, Advisory Senior Manager, Model Risk Oversight

"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."
—DAVID ASERMELY, Global Lead, Model Risk Management

Dettagli dell'edizione

• Editore: John Wiley & Sons Inc
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1119824931
• ISBN 13: 9781119824930
• Rilegatura: Copertina rigida
• Numero di pagine: 186
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile