Prediction of claims in export credit finance: A comparison of four machine learning techniques
| Autor: | Simone Krummaker, Mathias Bärtl |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
QA75
Computer science Strategy and Management Economics Econometrics and Finance (miscellaneous) claims prediction Decision tree Machine learning computer.software_genre lcsh:HG8011-9999 01 natural sciences HG HF5601 lcsh:Insurance 010104 statistics & probability Accounting 0502 economics and business ddc:330 050207 economics 0101 mathematics Artificial neural network business.industry Heuristic 05 social sciences Probabilistic logic Random forest export credit insurance machine learning Benchmark (computing) Credit insurance Test performance Artificial intelligence business computer |
| Zdroj: | Risks Volume 8 Issue 1 Risks, Vol 8, Iss 1, p 22 (2020) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|