---
title: "Credibility Clustering for Experience Rating: An Automated Approach to Risk Group Allocation in Group Life Insurance"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.07110v1"
permalink: "https://feed.astralor.com/items/itm_3d36bdaef6cdc7869592cf35"
published: "2026-11-04T20:24:47.000Z"
score: 57
channel: "tech"
tags: ["Credibility Clustering","Experience Rating","Automated Approach","Risk Group Allocation","Group Life Insurance","Within"]
---

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

## 速览

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 collectiv…

## 关键点

- 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.

## 全文

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.

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