AI-Driven Risk Modeling in Life Insurance: Advanced Techniques for Mortality and Longevity Prediction
Keywords:
Artificial Intelligence, Machine LearningAbstract
An accurate death and lifetime estimate helps life insurers be financially sound and provide competitive policies. Famous actuarial methods may miss lifespans and complex risk variable combinations. AI can change life insurance risk modeling. This research predicts life insurance underwriting and risk management mortality and lifetime using AI. Standard lifetime and mortality prediction actuarial methods are studied first. Decrement models, life tables, and stated death rates are used. These methods estimate population death trends using mortality data. They struggle to manage insurance data's volume and diversity.
The book addresses AI-driven risk modeling next. Artificial intelligence encompasses ML and deep learning. Machine learning predicts by finding patterns in prior data. Life insurance risk modeling uses ML approaches like Survival Analysis to forecast death probabilities. The Kaplan-Meier Estimator and Cox Proportional Hazards Model predict mortality and time-to-events.
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