Project Description: In a one-click shopping world with on-demand everything, the life insurance application process is antiquated. Customers provide extensive information to identify risk classification and eligibility, including scheduling medical exams, a process that takes an average of 30 days.
The result? People are turned off. That’s why only 40% of U.S. households own individual life insurance. Prudential wants to make it quicker and less labor intensive for new and existing customers to get a quote while maintaining privacy boundaries.
Project Objective: Develop a predictive model that accurately classifies risk using a more automated approach. The results will help Prudential better understand the predictive power of the data points in the existing assessment, enabling the company to significantly streamline the process.
Work Done:
• Performed data inspection, data cleaning, data transforming and modeling by implementing hypothesis testing, feature selection, data validation and machine learning algorithms
• Executed Principle Component Analysis for dimension reduction and resolved multicollinearity
• Applied machine learning models like- Logistic regression, SVM, XGboost and Decision Tree Regression
• Performed confusion matrix test to validate the predictor variables, applied visualization and presented using R-Shiny