Evaluation of risk level assessment strategies in life Insurance: A review of the literature

Abstract

The viability of every insurance company depends on risk assessment of new life policy proposals. Machine learning techniques are increasingly shown to double case processing speed, reducing manual evaluation time. The underwriter evaluates the risk in several ways, including financial and medical evaluations and category classification based on customer data and other factors like previous insurance information, clinical history, and financial data. This research examines different academics’ publications on risk prediction while offering a new insurance policy to an applicant. Multiple machine learning models developed by researchers have been extensively investigated. The researchers’ model evaluation criteria were analyzed to understand and discover study gaps. The article additionally analyses how researchers found an accurate machine-learning model. This report also analyses various scholars’ future work proposals to identify what could possibly be modified for further research. This study details the measures used by other academics to evaluate machine learning models. This study describes the criteria used by other scholars to evaluate machine learning models. The criteria used by investigators to assess the produced models were carefully evaluated to understand and spot any untapped potential for advancement. Researchers’ methods for finding an accurate machine-learning model are also examined in this article. In addition, this study analyses several researchers’ future work proposals to discover what may be changed for further research. Using previous academics’ work, this review suggests ways to enhance insurance manual procedures.