Explainable fintech lending

22 Pages Posted: 26 Jul 2021

See all articles by Golnoosh Babaei

Golnoosh Babaei

University of Pavia

Paolo Giudici

University of Pavia

Emanuela Raffinetti

University of Pavia

Date Written: July 24, 2021

Abstract

Lending to Small and Medium Enterprises (SME) is facilitated by the availability of advanced Machine Learning (ML) methods, embedded in financial technologies, which can accurately predict financial performance from the many data sources available. However, despite their high predictive accuracy, ML models may not provide sufficient explanations to investors and, therefore, may not be adequate for informed decision-making.

We propose a financial machine learning model that is both accurate and explainable. To reach this aim, we propose to enhance random forest models with a model selection procedure that progressively removes the least explainable variable, according to the Shapley value method.

We apply our proposal to 2,049 SMEs for which yearly financial performance indicators are available. Our results show that both the default and the expected return of SMEs can be well predicted and explained by a small set of indicators deduced from their balance sheets.

Keywords: Credit Risk Management, Explainable AI, Financial Technologies, Random Forest models.

JEL Classification: C18,G23

Suggested Citation

Babaei, Golnoosh and Giudici, Paolo and Raffinetti, Emanuela, Explainable fintech lending (July 24, 2021). Available at SSRN: https://ssrn.com/abstract=3892652 or http://dx.doi.org/10.2139/ssrn.3892652

Golnoosh Babaei

University of Pavia ( email )

Via San Felice
5
Pavia, Pavia 27100
Italy

Paolo Giudici (Contact Author)

University of Pavia ( email )

Via San Felice 7
27100 Pavia, 27100
Italy

Emanuela Raffinetti

University of Pavia ( email )

Via San Felice 5
Pavia, 27100
Italy

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