Explainable Machine Learning in Credit Risk Management

Niklas Bussmann (), Paolo Giudici, Dimitri Marinelli () and Jochen Papenbrock ()
Additional contact information
Niklas Bussmann: University of Pavia
Dimitri Marinelli: FinNet-Project
Jochen Papenbrock: FIRAMIS

Computational Economics, 2021, vol. 57, issue 1, No 9, 203-216

Abstract: Abstract The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. 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 their credit score and, therefore, to predict their future behaviour.

Keywords: Credit risk management; Explainable AI; Financial technologies; Similarity networks (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-020-10042-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10042-0

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-020-10042-0

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().