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Supplementing risk adjustment with high‐risk pooling using historical data for identifying the high risks

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  • Michel Oskam
  • Richard C. van Kleef
  • René C. J. A. van Vliet

Abstract

Many regulated health insurance markets with community‐rated premiums rely on risk adjustment (RA) to mitigate insurer‐incentives to risk select. However, insurers remain typically undercompensated for chronically ill enrollees. We use historical data on health spending and risk adjuster information to identify individuals undercompensated by the Dutch RA model of 2021 and find a selective group (1% of the population) with an average annual undercompensation of €6,050. We supplement the RA model with a risk sharing modality called high‐risk pooling (HRP) to organize residual‐based compensations towards insurers for the identified group to reduce the mean undercompensation to zero. The effects are evaluated on subgroups defined by chronic disease, finding a 42% reduction of their average undercompensation. Therefore, through compensating 1% of the population, the insurer‐incentives to select against chronically ill individuals substantially diminish. These results are compared to outlier‐risk sharing (reinsurance), proving HRP to be more effective at reducing selection incentives.

Suggested Citation

  • Michel Oskam & Richard C. van Kleef & René C. J. A. van Vliet, 2025. "Supplementing risk adjustment with high‐risk pooling using historical data for identifying the high risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 92(1), pages 166-202, March.
  • Handle: RePEc:bla:jrinsu:v:92:y:2025:i:1:p:166-202
    DOI: 10.1111/jori.12500
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