Credibility Clustering for Experience Rating: An Automated Approach to Risk Group Allocation in Group Life Insurance

Paper: arXiv 2611.07110

Authors: Thom van Rijn, Frank Weber, Michael Merz

Abstract

In insurance, experience rating plays a crucial role in mitigating adverse selection by adjusting premiums according to historical claims experience. A key step in this process is the classification of insured collectives into homogeneous risk groups. Within group life insurance, this classification traditionally relies on manual decisions and arbitrary thresholds, which may lead to inefficiencies and inconsistent outcomes. This paper proposes a data-driven methodology for automating risk group allocation based on a combination of the multidimensional Bühlmann-Straub model and k-means clustering – referred to as credibility clustering. By incorporating credibility weights, the method accounts for the varying statistical significance of experience data and ensuresthat risk classifications are both actuarially sound and practically implementable. Empirical results based on real-world group life insurance data from a major European insurer demonstrate that the proposed framework outperforms the two k-means benchmark variants considered in the empirical application in terms of viability, temporal stability and accounting for statistical reliability.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a robust methodology combining established actuarial theory with clustering, demonstrating strong empirical validation using real-world data. The mathematical formulation of the Bühlmann-Straub model is well-integrated, and the empirical results are thoroughly analyzed for viability and stability.

Research Flowchart

  flowchart TD
    A[Research Goal: Automate Risk Group Allocation in Group Life Insurance] --> B{Methodology: Credibility Clustering};
    B --> C[Inputs: Real-world Group Life Insurance Data];
    C --> D{Computational Process: <br/>1. Bühlmann-Straub Model (Credibility Weights) <br/>2. k-means Clustering};
    D --> E[Outcome: Data-driven Risk Group Allocation];
    E --> F[Key Findings: Outperforms benchmarks in viability, temporal stability, and statistical reliability.];