Beginning with coverage of data management in banking, the book goes on to discuss individual and multiple classifier approaches, reinforcement learning and AI in credit portfolio modelling, lifetime PD modelling, LGD modelling and EAD modelling. Fully worked examples in Python and R appear throughout the book, with source code provided on the companion website.
Machine Learning and Artificial Intelligence for Credit Risk Analytics fully covers the key concepts required to understand, challenge and validate credit risk models, whilst also looking to the future development of AI applications in credit risk management, demonstrating the need to embed economics and statistics to inform short, medium and long-term decision-making.
Beginning with coverage of data management in banking, the book goes on to discuss individual and multiple classifier approaches, reinforcement learning and AI in credit portfolio modelling, lifetime PD modelling, LGD modelling and EAD modelling. Fully worked examples in Python and R appear throughout the book, with source code provided on the companion website.
Machine Learning and Artificial Intelligence for Credit Risk Analytics fully covers the key concepts required to understand, challenge and validate credit risk models, whilst also looking to the future development of AI applications in credit risk management, demonstrating the need to embed economics and statistics to inform short, medium and long-term decision-making.
Machine Learning and Artificial Intelligence for Credit Risk Analytics: A Practical Guide with Examples Worked in Python and R
304
Machine Learning and Artificial Intelligence for Credit Risk Analytics: A Practical Guide with Examples Worked in Python and R
304Hardcover
Product Details
| ISBN-13: | 9781119781059 |
|---|---|
| Publisher: | Wiley |
| Publication date: | 01/27/2026 |
| Series: | The Wiley Finance Series |
| Pages: | 304 |
| Product dimensions: | 6.69(w) x 9.61(h) x (d) |