BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

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
Stock photo: cover may vary

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

by Terisa Roberts and Stephen J. Tonna

Add to wish list
  • New
New

Description

new.
Ask the seller a question Add to wish list
A$56.52
A$5.66 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)
The BIBLIO Guarantee

Details

  • Title Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning (Wiley and SAS Business Series)
  • Author Terisa Roberts and Stephen J. Tonna
  • Binding Hardback
  • Condition New
  • Pages 208
  • Volumes 1
  • Language ENG
  • Publisher Wiley
  • Features Index
  • Bookseller's Inventory # 42582155-n
  • 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 5

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

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

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

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.

Please wait a moment while we load the requested page.

tracking-