Classification of the Insureds Using Integrated Machine Learning Algorithms: A Comparative Study

ABSTRACT

With the growing number of insurance purchasers, the sophisticated claim analysis system has become an imperative must for any insurance firm. Claims Analysis can be utilized to better understand the customer strata and incorporate the findings throughout the insurance policy enrollment, including the underwriting and approval or rejection stages. In recent years machine learning (ML) technologies are increasingly being used to claims Analysis. However, choosing the optimal techniques, whether the features selection techniques, feature discretization techniques, resampling mechanisms, and ML classifiers for insurance decision assistance, is difficult and can harm the quality of claim suggestions. This study aims to develop appropriate decision models by combining binary classification, feature selection, feature discretization, and data resampling techniques. We did Extensive tests on three different datasets to evaluate the viability of the selected models. We used multiple assessment metrics besides the statistical significance test from The ANOVA test and the Friedman test to evaluate the ML models. The findings show that the models perform highly better after applying the feature discretization technique, reducing dimensionality using feature selection methods and solving the unbalanced data problem with resampling methods.

Data Availability Statement

Dataset_1: https://raw.githubusercontent.com/heathergeiger/Data621_hw4/master/insurance-evaluation-data.csv

Dataset_2: https://www.kaggle.com/prakharrathi25/premium-default-prediction/data

Dataset_3: https://www.kaggle.com/headsortails/steering-wheel-of-fortune-porto-seguro-eda/data

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by: The characteristic & preponderant discipline of key construction universities in Zhejiang province (Zhejiang Gongshang University- Statistics), Collaborative Innovation Center of Statistical Data Engineering Technology & Application, The National Natural Science Foundation of China (11971433).