Implementation of Correlation and Regression Models for Health Insurance Fraud in Covid-19 Environment using Actuarial and Data Science Techniques

TL;DR

This paper develops an enhanced fraud detection framework for health insurance during COVID-19, using actuarial and data science techniques, and finds a strong correlation between COVID-19 cases and insurance fraud rates.

Contribution

It introduces COVID-19 specific fraud triggers and models the relationship between COVID-19 and insurance fraud using correlation and regression analysis.

Findings

High correlation (0.86) between COVID-19 cases and fraud rates.

Regression model with R-squared of 0.91 indicates a good fit.

Enhanced fraud detection framework tailored for pandemic conditions.

Abstract

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.