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Artificial intelligence and credit riskthe use of alternative data and methods in internal credit rating /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence and credit riskby Rossella Locatelli, Giovanni Pepe, Fabio Salis.
Reminder of title:	the use of alternative data and methods in internal credit rating /
Author:	Locatelli, Rossella.
other author:	Pepe, Giovanni.
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 104 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	CreditManagement.
Online resource:	https://doi.org/10.1007/978-3-031-10236-3
ISBN:	9783031102363$q(electronic bk.)

Artificial intelligence and credit riskthe use of alternative data and methods in internal credit rating /
Locatelli, Rossella.

Artificial intelligence and credit riskthe use of alternative data and methods in internal credit rating /[electronic resource] :by Rossella Locatelli, Giovanni Pepe, Fabio Salis. - Cham :Springer International Publishing :2022. - xvii, 104 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. How AI Models are Built -- Chapter 3. AI Tools in Credit Risk -- Chapter 4. The Validation of AI Techniques -- Chapter 5. Possible Evolutions in AI Models.

This book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as "traditional" and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the new AI toolbox on new (unconventional) data to enhance the models' predictive power, without neglecting problems due to results' interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions. Rossella Locatelli is Full Professor of Banking at the University of Insubria, Italy. Giovanni Pepe is KPMG Partner since May 2015 where he works in the Financial Risk Management line of services with a focus on the quantitative aspects of credit risk. Fabio Salis is Chief Risk Officer of Creval since 2018. Formerly, he was Head of Risk Management at Banco Popolare since 2012, where he led important projects such as validation of credit and operational risk models and EBA stress test.

ISBN: 9783031102363$q(electronic bk.)

Standard No.: 10.1007/978-3-031-10236-3doiSubjects--Topical Terms:

174340
Credit
--Management.

LC Class. No.: HG3751 / .L63 2022

Dewey Class. No.: 658.88

Artificial intelligence and credit riskthe use of alternative data and methods in internal credit rating /
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