Muutke küpsiste eelistusi

Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning [Kõva köide]

4.00/5 (2 hinnangut Goodreads-ist)
,
  • Formaat: Hardback, 208 pages, kõrgus x laius x paksus: 231x158x23 mm, kaal: 386 g
  • Sari: Wiley and SAS Business Series
  • Ilmumisaeg: 26-Sep-2022
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119824931
  • ISBN-13: 9781119824930
Teised raamatud teemal:
  • Kõva köide
  • Hind: 46,14 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 54,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 3-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Raamatukogudele
Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep LearningSuurem pilt
  • Formaat: Hardback, 208 pages, kõrgus x laius x paksus: 231x158x23 mm, kaal: 386 g
  • Sari: Wiley and SAS Business Series
  • Ilmumisaeg: 26-Sep-2022
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119824931
  • ISBN-13: 9781119824930
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119824930.html
Märksõnad:

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.

Acknowledgments xi
Preface xiii
Chapter 1 Introduction
1(14)
Risk Modeling: Definition and Brief History
4(3)
Use of AI and Machine Learning in Risk Modeling
7(1)
The New Risk Management Function
7(3)
Overcoming Barriers to Technology and AI Adoption with a Little Help from Nature
10(1)
This Book: What It Is and Is Not
11(1)
Endnotes
12(3)
Chapter 2 Data Management and Preparation
15(16)
Importance of Data Governance to the Risk Function
18(2)
Fundamentals of Data Management
20(2)
Other Data Considerations for AI, Machine Learning, and Deep Learning
22(7)
Concluding Remarks
29(1)
Endnotes
30(1)
Chapter 3 Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management
31(24)
Risk Modeling Using Machine Learning
35(5)
Definitions of AI, Machine, and Deep Learning
40(12)
Concluding Remarks
52(1)
Endnotes
52(3)
Chapter 4 Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models
55(16)
Difference Between Explaining and Interpreting Models
57(2)
Why Explain AI Models
59(2)
Common Approaches to Address Explainability of Data Used for Model Development
61(1)
Common Approaches to Address Explainability of Models and Model Output
62(6)
Limitations in Popular Methods
68(1)
Concluding Remarks
69(1)
Endnotes
69(2)
Chapter 5 Bias, Fairness, and Vulnerability in Decision-Making
71(20)
Assessing Bias in AI Systems
73(3)
What Is Bias?
76(1)
What Is Fairness?
77(1)
Types of Bias in Decision-Making
78(11)
Concluding Remarks
89(1)
Endnotes
89(2)
Chapter 6 Machine Learning Model Deployment, Implementation, and Making Decisions
91(14)
Typical Model Deployment Challenges
93(5)
Deployment Scenarios
98(3)
Case Study: Enterprise Decisioning at a Global Bank
101(1)
Practical Considerations
102(1)
Model Orchestration
103(1)
Concluding Remarks
104(1)
Endnote
104(1)
Chapter 7 Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring
105(24)
Establishing the Right Internal Governance Framework
108(1)
Developing Machine Learning Models with Governance in Mind
109(3)
Monitoring AI and Machine Learning
112(10)
Compliance Considerations
122(3)
Further Takeaway
125(1)
Concluding Remarks
126(1)
Endnotes
127(2)
Chapter 8 Optimizing Parameters for Machine Learning Models and Decisions in Production
129(20)
Optimization for Machine Learning
131(2)
Machine Learning Function Optimization Using Solvers
133(3)
Tuning of Parameters
136(5)
Other Optimization Algorithms for Risk Models
141(2)
Machine Learning Models as Optimization Tools
143(4)
Concluding Remarks
147(1)
Endnotes
148(1)
Chapter 9 The interconnection between Climate and Financial instability
149(26)
Magnitude of Climate Instability: Understanding the "Why" of Climate Change Risk Management
152(5)
Interconnected: Climate and Financial Stability
157(1)
Assessing the impacts of climate change using AI and machine learning
158(2)
Using scenario analysis to understand potential economic impact
160(10)
Practical Examples
170(2)
Concluding Remarks
172(1)
Endnotes
172(3)
About the Authors 175(2)
Index 177
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.