Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection

TL;DR

This paper introduces a chaotic variational autoencoder (C-VAE) for one-class classification to detect insurance fraud, demonstrating superior performance over traditional VAE models on health and auto insurance datasets.

Contribution

The paper proposes a novel C-VAE model utilizing a logistic chaotic map for improved fraud detection in insurance, outperforming standard VAE methods.

Findings

C-VAE achieved 77.9% accuracy on health insurance fraud detection.

C-VAE achieved 87.25% accuracy on auto insurance fraud detection.

Statistical tests confirm C-VAE's significance over VAE.

Abstract

Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chaotic variational autoencoder (C-VAE to perform one-class classification (OCC) on genuine transactions. Here, we employed the logistic chaotic map to generate random noise in the latent space. The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets. We considered vanilla Variational Auto Encoder (VAE) as the baseline. It is observed that C-VAE outperformed VAE in both datasets. C-VAE achieved a classification rate of 77.9% and 87.25% in health and automobile insurance datasets respectively. Further, the t-test conducted at 1% level of significance and 18 degrees of freedom infers that C-VAE is statistically significant than the VAE.