Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning (Wiley and SAS Business Series) Paperback -
by Roberts, Terisa
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- Paperback
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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 Paperback
- Condition Used - Very good
- Pages 208
- Volumes 1
- Language ENG
- Publisher Wiley
- Features Index
- Bookseller's Inventory # GOR015028894
- 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)
- 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
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.
