#2254. Insurance fraud detection with unsupervised deep learning

June 2026publication date
Proposal available till 30-05-2025
5 total number of authors per manuscript2520 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Finance;
Accounting;
Economics and Econometrics;
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Abstract:
The objective of this paper is to propose a novel deep learning methodology to gain pragmatic insights into the behavior of an insured person using unsupervised variable importance. It lays the groundwork for understanding how insights can be gained into the fraudulent behavior of an insured person with minimum effort. Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Each models dynamics is discussed to inform the reader on how models can be adapted for fraud detection and how results can be perceived appropriately.
Keywords:
autoencoder; insurance fraud detection; unsupervised deep learning; variable importance; variational autoencoder

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