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Distributional refinement network: Distributional forecasting via deep learning

Author

Listed:
  • Avanzi, Benjamin
  • Dong, Eric T.
  • Laub, Patrick J.
  • Wong, Bernard

Abstract

A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalised Linear Models (GLMs) are commonly used, but challenges remain in developing models that can (i) allow covariates to flexibly impact different aspects of the conditional distribution, (ii) integrate developments in machine learning and AI to maximise the predictive power while considering (i), and, (iii) maintain a level of interpretability in the model to enhance trust in the model and its outputs, which is often compromised in efforts pursuing (i) and (ii).

Suggested Citation

  • Avanzi, Benjamin & Dong, Eric T. & Laub, Patrick J. & Wong, Bernard, 2026. "Distributional refinement network: Distributional forecasting via deep learning," Insurance: Mathematics and Economics, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:insuma:v:128:y:2026:i:c:s0167668726000363
    DOI: 10.1016/j.insmatheco.2026.103246
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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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