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Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied in this context, and there remains insufficient...
Accurate pricing of crop insurance policies relies on forecasts of probability densities of crop yields. Yield densities are dynamic, time series data on yields are often limited, and yield data are spatially correlated. We examine linear pooling of potentially related, but almost surely...
We analyze insurance demand when insurable losses come with an uninsurable zero‐mean background risk that increases in the loss size. If the individual is risk vulnerable, loss‐dependent background risk triggers a precautionary insurance motive and increases optimal insurance demand. Prudence...
We develop a novel machine learning (ML) framework to estimate a surrender charge for variable annuities (VAs) with the balance between human behavior and rational optimality. Optimality accounts for insurers' potential losses from strategic surrenders by policyholders who attempt to take...
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