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Boodhun, N and Jayabalan, M (2018) Risk prediction in life insurance industry using supervised learning algorithms. Complex & Intelligent Systems, 4 (2). pp. 145-154. ISSN 2199-4536
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Boodhun-Jayabalan2018_Article_RiskPredictionInLifeInsuranceI.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
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.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Springer |
| Date Deposited: | 05 Nov 2019 14:35 |
| Last Modified: | 04 Sep 2021 08:33 |
| DOI or ID number: | 10.1007/s40747-018-0072-1 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/11686 |
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