Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy
Keywords:
Annuity Products, Predictive Analytics, Machine LearningAbstract
Annuities provide retirement income, but their design and pricing are determined by risk and mortality prediction. Although consistent, conventional actuarial methods seldom include market volatility or personal risk profiles. Examined are advanced artificial intelligence and machine learning tools to enhance annuity prediction analytics for pricing and risk assessment.
The research begins with demographics, stationary death, and annuity pricing. Examined is then the theoretical foundation of artificial intelligence and machine learning techniques like Random Forests and unsupervised tools like k-means clustering as well as Gradient Boosting Machines (GBMs). From complex data sources like financial histories and medical records, RNNs and CNNs find latent trends and improve risk prediction.
This work finds, modifies, generates model training data via feature engineering. To improve risk granularity, the paper investigates feature engineering annuity pricing utilizing socioeconomic, lifestyle, and healthcare use data.
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