New credibility clustering method automates insurance risk allocation

A new paper proposes an automated method called "credibility clustering" for risk group allocation in group life insurance. This data-driven approach combines the Bühlmann-Straub model with k-means clustering to classify insured collectives into homogeneous risk groups. The method incorporates credibility weights to account for the statistical significance of historical claims data, aiming for more actuarially sound and practically implementable risk classifications. Empirical results from a European insurer indicate that this framework outperforms traditional k-means benchmarks in terms of viability, temporal stability, and statistical reliability. AI

IMPACT This research could lead to more efficient and accurate risk assessment in the insurance industry, potentially impacting premium setting and underwriting processes.

RANK_REASON Academic paper proposing a new methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

• Bühlmann-Straub model
• credibility clustering
• group life insurance
• k-means clustering

• paper
• other

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