Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Mathias Bärtl and Simone Krummaker
Additional contact information
Mathias Bärtl: Hochschule für Technik, Wirtschaft und Medien Offenburg, 77652 Offenburg, Germany
Simone Krummaker: Faculty of Actuarial Science and Insurance, Cass Business School, University of London, London EC1Y8TZ, UK

Risks, 2020, vol. 8, issue 1, 1-27

Abstract: This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.

Keywords: machine learning; claims prediction; export credit insurance (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/2227-9091/8/1/22/pdf (application/pdf)
https://www.mdpi.com/2227-9091/8/1/22/ (text/html)

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:gam:jrisks:v:8:y:2020:i:1:p:22-:d:326934

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().