 The value of cross-data set analysis for automobile insurance fraud detectionMeryem Yankol-Schalck Research in International Business and Finance, 2022, vol. 63, issue C Abstract: This study focuses on personal automobile policies underwritten. Its aim is to provide decision support and to apply new models with good predictive performance and high operational efficiency. We propose a new approach by constructing a score that evolves over the life of a claim. It consists of creating a score at the opening of a claim and another derived from the information of the first adjuster’s report. Natural language processing is also used on a textual variable relating to the description of the claim provided by the agency. The fraud score is estimated by using a gradient boosting machine (GBM) and a neural network. The results are interpreted using the local interpretable model-agnostic explanations (LIME). They show that fraud detection is improved when all the information and the textual variable are included. Furthermore, we observe that the GBM method overperforms the neural network approach. Keywords: Fraud detection; Automobile insurance; Cross-data set; Natural language processing; Boosting; Neutral network (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:63:y:2022:i:c:s0275531922001556 DOI: 10.1016/j.ribaf.2022.101769 Access Statistics for this article Research in International Business and Finance is currently edited by T. Lagoarde Segot More articles in Research in International Business and Finance from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.