Introduction

Rossella Locatelli (), Giovanni Pepe () and Fabio Salis ()
Additional contact information
Rossella Locatelli: University of Insubria
Giovanni Pepe: KPMG Advisory
Fabio Salis: Credito Valtellinese

Chapter Chapter 1 in Artificial Intelligence and Credit Risk, 2022, pp 1-7 from Springer

Abstract: Abstract Nowadays that new masses of data have become available, and the artificial intelligence (AI) techniques have become more interpretable, the financial service industry is investing on the development of AI models. In particular, alternative types of information have become accessible due to the business relationships of banks with their customers, the progressive digitalisation of the economy, the availability of information of the websites, newspaper articles and social media, the COVID-19 pandemics. Those types of data can be used with different purposes to enhance several aspects of the credit risk modelling: early warning, provisioning, benchmarking, loan granting and risk discrimination.

Keywords: Digitalised data; Artificial intelligence; Machine learning; Processing capabilities; Alternative information; PSD2; Scoring; Early warning systems; High-frequency data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10236-3_1

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DOI: 10.1007/978-3-031-10236-3_1

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