This chapter examines the use of machine learning (ML) models in credit scoring and risk assessment, comparing their efficiency and accuracy to traditional methods. The discussion highlights ML's potential to enhance credit evaluations, particularly in areas like peer-to-peer lending and non-traditional financial products. The analysis emphasizes the operational benefits of adopting ML in credit processes.

Manta, F., Calò, L., Stefanelli, V., Cotugno, M., Boscia, V., Machine Learning in support of credit scoring overcoming traditional predictive models. What do we know so far?, in Geretto Enrico Fioravanti, P. E. (ed.), Innovation in Banking and Financial Intermediaries, Routledge, London 2025: 189- 215 [https://hdl.handle.net/10807/322217]

Machine Learning in support of credit scoring overcoming traditional predictive models. What do we know so far?

Cotugno, Matteo;
2025

Abstract

This chapter examines the use of machine learning (ML) models in credit scoring and risk assessment, comparing their efficiency and accuracy to traditional methods. The discussion highlights ML's potential to enhance credit evaluations, particularly in areas like peer-to-peer lending and non-traditional financial products. The analysis emphasizes the operational benefits of adopting ML in credit processes.
2025
Inglese
Innovation in Banking and Financial Intermediaries
9781032887968
Routledge
Manta, F., Calò, L., Stefanelli, V., Cotugno, M., Boscia, V., Machine Learning in support of credit scoring overcoming traditional predictive models. What do we know so far?, in Geretto Enrico Fioravanti, P. E. (ed.), Innovation in Banking and Financial Intermediaries, Routledge, London 2025: 189- 215 [https://hdl.handle.net/10807/322217]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/322217
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact