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The risk of artificial intelligence in credit ratings = exploring the efficiency, development and impact /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The risk of artificial intelligence in credit ratings/ by Daniel Cash, Nataliya Tkachenko.
Reminder of title:	exploring the efficiency, development and impact /
Author:	Cash, Daniel.
other author:	Tkachenko, Nataliya.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 100 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction -- Chapter 2 Generative AI: Concept, Applications, and Implications -- Chapter 3 The Growing Adoption of AI within the World of Credit Ratings -- Chapter 4 The Regulatory Perspective -- Chapter 5 Recommendations -- Chapter 6 Conclusion.
Contained By:	Springer Nature eBook
Subject:	Credit bureaus - Risk management. -
Online resource:	https://doi.org/10.1007/978-3-031-95543-3
ISBN:	9783031955433

The risk of artificial intelligence in credit ratings = exploring the efficiency, development and impact /
Cash, Daniel.

The risk of artificial intelligence in credit ratingsexploring the efficiency, development and impact /[electronic resource] :by Daniel Cash, Nataliya Tkachenko. - Cham :Springer Nature Switzerland :2025. - xiii, 100 p. :ill. (some col.), digital ;24 cm.

Chapter 1 Introduction -- Chapter 2 Generative AI: Concept, Applications, and Implications -- Chapter 3 The Growing Adoption of AI within the World of Credit Ratings -- Chapter 4 The Regulatory Perspective -- Chapter 5 Recommendations -- Chapter 6 Conclusion.

As the leading credit rating agencies begin to heavily invest in the adoption of artificial intelligence, historic systemic failures serve as a reminder of the effect of mis-regulation and misdiagnosis in the credit rating world. As the industry turns towards technologies that can massively enhance the speed, efficiency, but also the temptation to transgress within the credit rating world, there are critical questions that need to be asked to shape the response that will be needed. For regulators and policymakers, the multivariant threat that the adoption of artificial intelligence within the credit rating world poses will require an extensive but nuanced response to counter it. This book presents these issues, reveals intricate implications, and provides for a considered response that regulators and policymakers should consider. Daniel Cash is Reader in Law at Aston University and a Senior Fellow at the United Nations University Centre for Policy Research. Daniel's research is exclusively concerned with the regulation of the credit rating industry, with a wider focus on the financial regulation of financial service providers, and the relationship between the financial sector and its impact upon society. He has authored a number of books, edited collections, and articles on the credit rating industry specifically. Nataliya Tkachenko is an AI strategy lead for sustainable finance at the AI Centre of Excellence (Lloyds Banking Group). She is also a visiting fellow at the Cambridge Centre for Finance, Technology and Regulation (University of Cambridge Judge Business School) and an Executive Director of UK Multimodal AI Network, funded by EPSRC. She obtained her PhD in Computer Science from the University of Warwick in 2019, and continues to pursue her interest in how AI transforms financial industry, what are the biggest opportunities and associated risks. She's part of AI Assurance working groups within DRCF and IOSCO.

ISBN: 9783031955433

Standard No.: 10.1007/978-3-031-95543-3doiSubjects--Topical Terms:

3787677
Credit bureaus
--Risk management.

LC Class. No.: HG3751.5

Dewey Class. No.: 658.155

The risk of artificial intelligence in credit ratings = exploring the efficiency, development and impact /
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