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AI Tools in Credit Risk

In: Artificial Intelligence and Credit Risk

Author

Listed:
  • Rossella Locatelli

    (University of Insubria)

  • Giovanni Pepe

    (KPMG Advisory)

  • Fabio Salis

    (Credito Valtellinese)

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.

Suggested Citation

  • Rossella Locatelli & Giovanni Pepe & Fabio Salis, 2022. "AI Tools in Credit Risk," Springer Books, in: Artificial Intelligence and Credit Risk, chapter 0, pages 29-64, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-10236-3_3
    DOI: 10.1007/978-3-031-10236-3_3
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