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Financial Modeling Using Quantum Computing

You're reading from   Financial Modeling Using Quantum Computing Design and manage quantum machine learning solutions for financial analysis and decision making

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618424
Length 292 pages
Edition 1st Edition
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Authors (4):
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Iraitz Montalban Iraitz Montalban
Author Profile Icon Iraitz Montalban
Iraitz Montalban
Anshul Saxena Anshul Saxena
Author Profile Icon Anshul Saxena
Anshul Saxena
Javier Mancilla Javier Mancilla
Author Profile Icon Javier Mancilla
Javier Mancilla
Christophe Pere Christophe Pere
Author Profile Icon Christophe Pere
Christophe Pere
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: Basic Applications of Quantum Computing in Finance
2. Chapter 1: Quantum Computing Paradigm FREE CHAPTER 3. Chapter 2: Quantum Machine Learning Algorithms and Their Ecosystem 4. Chapter 3: Quantum Finance Landscape 5. Part 2: Advanced Applications of Quantum Computing in Finance
6. Chapter 4: Derivative Valuation 7. Chapter 5: Portfolio Management 8. Chapter 6: Credit Risk Analytics 9. Chapter 7: Implementation in Quantum Clouds 10. Part 3: Upcoming Quantum Scenario
11. Chapter 8: Simulators and HPC’s Role in the NISQ Era 12. Chapter 9: NISQ Quantum Hardware Roadmap 13. Chapter 10: Business Implementation 14. Index 15. Other Books You May Enjoy

The relevance of credit risk analysis

With the objective of providing a broader context and understanding of the relevance of addressing classification problems in the finance sector, for this part of the book, it is important to define some core concepts, even from a high-level perspective. The term “credit risk” in the context of this chapter is the chance that a lender will lose money if a borrower doesn’t pay back a loan by a certain date. As the credit card business has grown quickly, as illustrated in Figure 6.1, and the financial players have grown over the years, the challenge of expanding the scope of targeted people requires more sophisticated underwriting systems. This puts a big portion of financial institutions at risk if the means to assess this risk are not accurate enough.

Given the situation previously described, it is often necessary to look at the credit risk of customers who have little or no credit history to expand the current client segments...

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