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Paper / 2026SMQ / 183132273416997
Reinforcement Learning for Micro-Level Claims Reserving
Benjamin Avanzi / Bernard Wong / Mario Wüthrich / Ronald Richman / Yagebu Xie
insurance / reinforcement learningResource overview

Reinforcement Learning for Micro-Level Claims Reserving

Paper2026

by Benjamin Avanzi, Bernard Wong, Mario Wüthrich, Ronald Richman, Yagebu Xie

Tags
insurancereinforcement learning

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Suggested foundations for risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  2. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

Go further

  1. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

  2. Backtesting VaR and ES: Coverage Tests and Model Validation

    Check forecasts against realised losses.

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