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New boosting approach models insurance claim frequency and severity

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new boosting approach for fitting structured additive distributional copula regression models. This method is designed to jointly model insurance claim frequency and severity, extending generalized additive models (GAMLSS) to allow multiple distribution parameters to depend on covariates. The proposed framework offers greater transparency and auditability compared to black-box alternatives and has demonstrated competitive predictive performance on synthetic and real-world data. AI

IMPACT This research introduces a more interpretable and transparent method for statistical modeling, potentially improving risk assessment in insurance.

RANK_REASON The cluster contains an academic paper detailing a new statistical modeling approach. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New boosting approach models insurance claim frequency and severity

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The cluster contains an academic paper detailing a new statistical modeling approach. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Banghee So, Na Zhang ·

    Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting

    arXiv:2610.36835v1 Announce Type: new Abstract: In this paper, we propose a model-based boosting approach for fitting structured additive distributional copula regression models with bivariate responses, focusing particularly on the joint modeling of claim frequency and claim sev…

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