A Novel Two-way cross-tab Machine Learning Approach for predicting Life Insurance using Bivariate Exploratory Analysis

Abstract

Life Insurance prediction is the main objective of the research: to evaluate a person’s life insurance using a machine learning model. In day-to-day life human life becomes hectic and the life span of everyone gets reduced due to pandemic situations, unavoidable accidents and historical impacts, etc. Even though the security and saving beneficiaries are there in life insurance, risk factors are also associated with it. Machine learning techniques propose a risk reduction avoidance and prediction of financial scams to save customer’s lives when individual customers claim their own experience in risk factors by sharing their own credentials. The analysis is made by logistic regression based on the probability of categorical data such as identity proof, Aadhar proof, PAN number, and so on as attribute value. Using the novel Two-way cross-tab method the relationships based on attribute value matrix value are generated to find the customer who has no mutual identifier to take the life insurance cash and summarize the prediction using the bivariate exploratory flow of graph. By seeing the difference in the relationship, the threats and risk full factors can be reduced in accuracy compared to existing machine learning models.