Claims fraud detection with uncertain labels

Félix Vandervorst (), Wouter Verbeke () and Tim Verdonck ()
Additional contact information
Félix Vandervorst: KU Leuven
Wouter Verbeke: KU Leuven
Tim Verdonck: University of Antwerp

Advances in Data Analysis and Classification, 2024, vol. 18, issue 1, No 10, 219-243

Abstract: 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.

Keywords: Insurance claims fraud; Soft labels; Machine learning; Nonlife insurance; 68T37; -; Reasoning; under; uncertainty; in; the; context; of; artificial; intelligence (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11634-023-00568-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:18:y:2024:i:1:d:10.1007_s11634-023-00568-0

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-023-00568-0

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().