Leveraging Predictive Analytics and Clustering for Personalized Financial Planning in Life Insurance: A Data-Driven Approach to Tailored Policyholder Advice

Authors: Preetham Reddy Kaukuntla

DOI: https://doi.org/10.5281/zenodo.14763332

Short DOI: https://doi.org/g83jdc

Country: USA

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Abstract: The life insurance business is in the process of change in response to data techniques, which makes it possible to engage and advise customers more effectively. This paper aims at analyzing the use of predictive analytics and clustering for probable improvement in the financial forecasting of life insurance policyholders. The key methods looked at include k-means clustering, hierarchical clustering as well as regression models that sort the policyholders into segments by demography, spending power or major life events. These techniques are discussed in this paper with respect to the results they have in enhancing customer segmentation, personalization of financial solutions, and customer loyalty. Optimal reinsurance structures are then illustrated using real-life insurance data to show the possibility of real-life applications of these techniques for insurance analysis and financial planning. The study reveals that PA and clustering represents vital informative solutions in the field of life insurance as far as it is focused on the customer satisfaction and company’s successful functioning in terms of profitability.

Keywords: Decision Trees, Operating & Regulatory, Market Overview, KPI Selection in Life Insurance, Financial Planning & Analysis, Business Objectives & Metrics, Predictive Modelling, Customer Segmentation, Financial Analytics, Insurance Policyholder Profitability, Life Insurance Analytics.

Paper Id: 232077

Published On: 2020-06-10

Published In: Volume 8, Issue 3, May-June 2020

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