  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.
