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.

翻译：COVID-19给健康保险业务带来了更多的潜在欺诈挑战。这项工作描述了在前COVID-19和COVID-19环境中实施现有和强化的欺诈检测方法的情况。为此目的,我们利用精算和数据科学技术开发了一个创新的强化欺诈检测框架。在现有的触发因素之外,还确定了COVID-19特有的触发因素。我们还探讨了保险欺诈与COVID-19案件之间的关系。为了确定我们计算出的Pearson相关系数和医疗保险欺诈与COVID-19案件之间的对数回归模型。这项工作使用了两种数据集:健康保险数据集和印度同一特定地理位置的COVID-19案件Kagle数据集。我们的实验结果显示Pearson关联系数为0.86,这意味着每月欺诈案件的比率与每月的COVID-19案件的比率高度相关。在数据上进行的对数回归使Rearson相关系数和对数回归模型在健康保险欺诈和COVID-19案件之间具有匹配的回归模型。这项工作使用两种数据集:健康保险数据集和Kaglegled数据集,用于印度同一特定地理位置的COVID-19案件。我们的实验结果显示Pearson相关系数为0.86,这意味着,每月欺诈案件的比率与月份的月比率与每月的汇率高度相关。这个模型与每月的对比。