  Fraud acts as a major deterrent to a companys growth if uncontrolled. It
challenges the fundamental value of Trust in the Insurance business. COVID-19
brought additional challenges of increased potential fraud to health insurance
business. This work describes implementation of existing and enhanced fraud
detection methods in the pre-COVID-19 and COVID-19 environments. For this
purpose, we have developed an innovative enhanced fraud detection framework
using actuarial and data science techniques. Triggers specific to COVID-19 are
identified in addition to the existing triggers. We have also explored the
relationship between insurance fraud and COVID-19. To determine this we
calculated Pearson correlation coefficient and fitted logarithmic regression
model between fraud in health insurance and COVID-19 cases. This work uses two
datasets: health insurance dataset and Kaggle dataset on COVID-19 cases for the
same select geographical location in India. Our experimental results shows
Pearson correlation coefficient of 0.86, which implies that the month on month
rate of fraudulent cases is highly correlated with month on month rate of
COVID-19 cases. The logarithmic regression performed on the data gave the
r-squared value of 0.91 which indicates that the model is a good fit. This work
aims to provide much needed tools and techniques for health insurance business
to counter the fraud.
