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

  • 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 datasetThis 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.show moreshow less

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Author:Mathias BärtlGND, Simone Krummaker
Publisher:mdpi
Place of publication:Basel
Year of Publication:2020
Pagenumber:27
Language:English
GND Keyword:Exportkredit; Maschinelles Lernen; Versicherung
Tag:Claims; Export Credit; Machine Learning; Prediction
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
300 Sozialwissenschaften
Parent Title (English):Risks
Volume:8
Issue:1
ISSN:2227-9091
First Page:Artikel 22
Document Type:Article (reviewed)
Open Access:Frei zugänglich
Institutes:Bibliografie
Release Date:2020/12/07
Licence (German):License LogoCreative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International
DOI:https://doi.org/10.3390/risks8010022