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Financial Modeling Using Quantum Computing

Financial Modeling Using Quantum Computing

By : Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere
5 (8)
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Financial Modeling Using Quantum Computing

Financial Modeling Using Quantum Computing

5 (8)
By: Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere

Overview of this book

Quantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems. This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you’ll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing. By the end of this book, you’ll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.
Table of Contents (16 chapters)
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1
Part 1: Basic Applications of Quantum Computing in Finance
5
Part 2: Advanced Applications of Quantum Computing in Finance
10
Part 3: Upcoming Quantum Scenario

Credit Risk Analytics

Problems such as credit scoring, fraud detection, churn prediction, credit limit definition, and financial behavior forecasting (among others) are constant challenges for banks and financial institutions, which permanently research for more accurate results and ways to decrease business-related risk when providing services. Most of these problems can be tackled by using machine learning to classify users who are likely to, for example, not pay their bills on time or commit fraud. In this chapter, the quantum machine learning side of these scenarios will be explored, using a permanent benchmark with classical counterparts for most of the cases.

In the current economic situation, where the stability of the markets is unpredictable and the way people work is always changing (thanks to the rise of the “gig economy”), it is harder to increase a credit product portfolio and cover a larger number of customer cohorts without increasing the risk for businesses...

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