Publication
Title
Claims fraud detection with uncertain labels
Author
Abstract
Insurance fraud is a non self-revealing type of fraud. The true historical labels (fraud or legitimate) are only as precise as the investigators' efforts and successes to uncover them. Popular approaches of supervised and unsupervised learning fail to capture the ambiguous nature of uncertain labels. Imprecisely observed labels can be represented in the Dempster-Shafer theory of belief functions, a generalization of supervised and unsupervised learning suited to represent uncertainty. In this paper, we show that partial information from the historical investigations can add valuable, learnable information for the fraud detection system and improves its performances. We also show that belief function theory provides a flexible mathematical framework for concept drift detection and cost sensitive learning, two common challenges in fraud detection. Finally, we present an application to a real-world motor insurance claim fraud.
Language
English
Source (journal)
Advances in data analysis and classification. - Berlin, 2007, currens
Publication
Heidelberg : Springer heidelberg , 2023
ISSN
1862-5347 [print]
1862-5355 [online]
DOI
10.1007/S11634-023-00568-0
Volume/pages
(2023) , p. 1-25
ISI
001110976400001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 09.01.2024
Last edited 19.01.2024
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