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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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Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/4317
Bibliografische Angaben
Title (English):Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques
Author:Mathias BärtlStaff MemberGND, Simone Krummaker
Year of Publication:2020
Place of publication:Basel
Publisher:mdpi
Page Number:27
Article Number:22
Parent Title (English):Risks
Volume:8
Issue:1
ISSN:2227-9091
DOI:https://doi.org/10.3390/risks8010022
URN:https://urn:nbn:de:bsz:ofb1-opus4-43170
Language:English
Inhaltliche Informationen
Institutes:Forschung / IfTI - Institute for Trade and Innovation
Fakultät Wirtschaft (W)
Institutes:Bibliografie
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
300 Sozialwissenschaften
GND Keyword:Exportkredit; Maschinelles Lernen; Versicherung
Tag:Claims; Export Credit; Machine Learning; Prediction
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International