Predicting Customer Attrition in Claims and Direct Billing Services of Health Insurance

DOI

10.2991/978-94-6463-583-6_23How to use a DOI?

Keywords

Claims; Direct Billing Service; Health Insurance; Customer Segmentation; Machine Learning

Abstract

This paper proposes an approach for predicting the likelihood of customers discontinuing their use of claims and direct billing services in health insurance. Focused on delineating customer profiles and segmentation based on service usage behavior, we leverage a comprehensive dataset detailing customers’ insurance participation history. We utilize advanced machine learning techniques including K-Means clustering to analyze this data effectively. Additionally, four predictive models including Logistic Regression, Random Forest, k-nearest Neighbor and XGBoost are rigorously tested to ascertain their predictive accuracy and reliability. The critical aspect of this study involves employing various evaluation methodologies to determine which model offers the most accurate prediction. The experimental result shows that the Random Forest model stands out, delivering the highest performance index among the evaluated models. This model's ability to handle complex data patterns makes it particularly effective for this application. Moreover, the study extends beyond mere prediction. It classifies customers into four distinct segments, thereby providing a nuanced understanding of different customer groups. This classification is pivotal for developing targeted and effective customer retention strategies for each segment, underscoring the practical implications of the research for health insurance companies.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Trung-Duy Nguyen
AU  - Thanh-Hieu Bui
PY  - 2024
DA  - 2024/11/26
TI  - Predicting Customer Attrition in Claims and Direct Billing Services of Health Insurance
BT  - Proceedings of the 2nd International Conference - Resilience by Technology and Design (RTD 2024)
PB  - Atlantis Press
SP  - 450
EP  - 471
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-583-6_23
DO  - 10.2991/978-94-6463-583-6_23
ID  - Nguyen2024
ER  -