Actuarial Data Analytics for Life Insurance Product Development: Techniques, Models, and Real-World Applications
Keywords:
Actuarial Data Analytics, Life Insurance Product Development, Predictive ModelingAbstract
Competition, laws, and consumer expectations change life insurance. Actuarial data analytics helps insurers compete and provide innovative services. This work builds, validates, and executes life insurance models using actuarial data analytics. Historical data and actuarial skills were used to evaluate mortality risk, price policies, and provide features for life insurance products. Data explosion has improved analytics, but this method is still important. Actuarial data analytics uses statistics and machine learning to analyze large datasets. These discoveries improve actuarial methodologies and let insurers create more complex, customer-focused solutions.
Predictive modeling uses data analytics. Insurers may use historical mortality data, socio-economic characteristics, anonymised and controlled health data, and lifestyle behaviors to predict death. Risk profiles are analyzed and costs are adjusted for each covered individual. Risk-based pricing favors price transparency and fairness over one-size-fits-all.
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