Forecasting Member Churn in Medical Insurance through Machine Learning Analysis
The insurance industry faces an escalating challenge with increasing customer churn, spurred by global advancements in technology. The ease with which customers can compare policies, explore new offers, and switch providers online has intensified industry competition. This phenomenon has led t...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en en |
| Published: |
INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1833/1/ij2023_65r.pdf http://eprints.intimal.edu.my/1833/2/130 http://eprints.intimal.edu.my/1833/ https://intijournal.intimal.edu.my |
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| Summary: | The insurance industry faces an escalating challenge with increasing customer churn, spurred by
global advancements in technology. The ease with which customers can compare policies, explore
new offers, and switch providers online has intensified industry competition. This phenomenon
has led to substantial revenue loss for many companies, as acquiring new customers often incurs
higher costs than retaining existing ones. Recognizing the paramount importance of client
retention, this research addresses the issue by proposing a Churn Prediction System tailored for
the medical insurance sector. The system leverages machine learning models to forecast whether
an existing customer is likely to churn, crucial for proactive retention strategies. To determine the
most effective algorithm for this task, four models—Logistic Regression, Random Forest Decision
Tree, Support Vector Machine, and Artificial Neural Network—are tested. The Random Forest
Classifier emerges as the optimal performer which achieve accuracy of 90%. |
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