Machine learning et nouvelles sources de données pour le scoring de crédit

Christophe Hurlin and Christophe Perignon ()

Revue d'économie financière, 2019, vol. N° 135, issue 3, 21-50

Abstract: In this article, we discuss the contribution of machine learning techniques and new data sources (new data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of machine learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of machine learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through machine learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers? creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, machine learning applied to these data can also lead to severe biases and discrimination. Classification JEL : C10, C38, C55, G21, G29.

JEL-codes: C10 C38 C55 G21 G29 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Machine learning et nouvelles sources de données pour le scoring de crédit (2019)
Working Paper: Machine Learning et nouvelles sources de données pour le scoring de crédit (2019)
Working Paper: Machine Learning et nouvelles sources de données pour le scoring de crédit (2019)
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