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.