Abstract
Risk assessment is a crucial element in the life insurance business to classify the applicants. Companies perform underwriting process to make decisions on applications and to price policies accordingly. With the increase in the amount of data and advances in data analytics, the underwriting process can be automated for faster processing of applications. This research aims at providing solutions to enhance risk assessment among life insurance firms using predictive analytics. The real world dataset with over hundred attributes (anonymized) has been used to conduct the analysis. The dimensionality reduction has been performed to choose prominent attributes that can improve the prediction power of the models. The data dimension has been reduced by feature selection techniques and feature extraction namely, Correlation-Based Feature Selection (CFS) and Principal Components Analysis (PCA). Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants. Findings revealed that REPTree algorithm showed the highest performance with the lowest mean absolute error (MAE) value of 1.5285 and lowest root-mean-squared error (RMSE) value of 2.027 for the CFS method, whereas Multiple Linear Regression showed the best performance for the PCA with the lowest MAE and RMSE values of 1.6396 and 2.0659, respectively, as compared to the other models.
Highlights
The big data technologies revolutionize the way insurance companies to collect, process, analyze, and manage data more efficiently [1,2]
Individual life insurance organizations still rely on the conventional actuarial formulas to predict mortality rates and premiums of life policies
Life insurance companies have recently started carrying out predictive analytics to improve their business efficacy, but there is still a lack of extensive research on how predictive analytics can enrich the life insurance domain
Summary
The big data technologies revolutionize the way insurance companies to collect, process, analyze, and manage data more efficiently [1,2]. Proliferate in various sectors of insurance industries such as risk assessment, customer analytics, product development, marketing analytics, claims analysis, underwriting analysis, fraud detection, and reinsurance [3,4]. Telematics is a typical example where big data. Life insurance companies have recently started carrying out predictive analytics to improve their business efficacy, but there is still a lack of extensive research on how predictive analytics can enrich the life insurance domain. Researchers have concentrated on data mining techniques to detect frauds among insurance firms, which is a crucial issue due to the companies facing great losses [6,7,8]
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