Explainable Machine learning in Credit Risk Management

21 Pages Posted: 10 Jan 2020 Last revised: 2 Jul 2020

Date Written: December 18, 2019

Abstract

The paper proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms. The model applies similarity networks to Shapley values, so that AI predictions are grouped according to the similarity in the underlying explanatory variables.

The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain and understand their credit score and, therefore, to predict their future behaviour.

Keywords: Credit risk management, Explainable AI , Financial Technologies , Similarity networks, Financial Networks, Machine Learning

JEL Classification: G00, G32, O33, C4, C69

Suggested Citation

Bussmann, Niklas and Giudici, Paolo and Marinelli, Dimitri and Papenbrock, Jochen, Explainable Machine learning in Credit Risk Management (December 18, 2019). Available at SSRN: https://ssrn.com/abstract=3506274 or http://dx.doi.org/10.2139/ssrn.3506274

Niklas Bussmann

University of Pavia ( email )

Corso Strada Nuova, 65
27100 Pavia, 27100
Italy

Paolo Giudici

University of Pavia ( email )

Corso Strada Nuova, 65
27100 Pavia, 27100
Italy

Dimitri Marinelli

Munich Re ( email )

K├Âniginstr. 107
Munich, 80802
Germany

Jochen Papenbrock (Contact Author)

Firamis ( email )

Robert-Kempner-Ring 27
Oberursel, 61440
Germany
+49 174 143 5555 (Phone)

HOME PAGE: http://www.firamis.de

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