AI Tools in Credit Risk

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

Chapter Chapter 3 in Artificial Intelligence and Credit Risk, 2022, pp 29-64 from Springer

Abstract: Abstract This chapter describes four types of application of AI into Credit Risk modelling. The use of alternative transactional data together with the application of machine learning techniques in the context of the Probability of Default (PD) parameter estimation leads to enhancements of the PD models, able to capture phenomena that were not properly explained by the traditional models. Some examples are described in this paragraph: risk discrimination for borrowers with seasonal business, identification of counterparty risk during the COVID-19 crisis, early warnings and advanced analytics in loan approval Several combinations of traditional modelling techniques and AI techniques can be used to enhance the outcome of the credit risk models. In particular, the business case “two-step approach” is described, detailing the intervention of the AI techniques in a second phase of the model estimation, when the traditional techniques already produced a result. The third part of the chapter describes the application of an AI model to asset management. The model is aimed at supporting an asset manager’s investment decisions. The last section of the chapter describes how to implement machine learning techniques with benchmarking purposes in the context of the validation of credit risk models used for the estimation of the regulatory capital.

Keywords: Probability of default—PD; Machine learning—ML; Structured information; Unstructured information; Transactional data; Current account transactional data; POS transactional data; Payment cards; Social network data; Interpretability of results; SHAP summary plot; Early warning; COVID-19; Advanced analytics; Loan approval; Risk discrimination; Seasonal business; Counterparty risk; Business case; Use case; Artificial intelligence—AI; Traditional modelling techniques; Traditional techniques; Logistic regression; Logistic model; Linear model; Linear regression; Two-step approach; Combination of techniques; Business case; Random forest; Asset management; Semi-liquid investment grade; Illiquid investment grade; Unstructured data; Neural network; Deep neural network; Validation; Credit risk models; Credit risk; Benchmarking; Credit risk assessment; Traditional model; Traditional modelling techniques; Managerial model; Regulatory capital (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_3

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DOI: 10.1007/978-3-031-10236-3_3

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