A Longitudinal Attribute-Conditioned Neural Network for Modeling Health-State Transition Probabilities in Temporally Irregular Data: The LANTERN Framework
arXiv:2606.13880v1 Announce Type: new Abstract: Accurate estimation of long-term care transition probabilities is central to disability insurance pricing, reserving, and solvency assessment. Classical actuarial multi-state models commonly rely on Markov, semi-Markov, or proportional-hazard specifications, which provide a direct connection to cohort projection but may be restrictive for irregular longitudinal health data with nonlinear aging patterns and heterogeneous covariate histories. This paper develops a well-calibrated estimator of multi-state transition probabilities for irregular longi
The increasing availability of irregular longitudinal health data and advances in neural network capabilities are enabling more sophisticated modeling approaches for complex actuarial problems.
This development offers a significant improvement in the accuracy of disability insurance pricing and solvency assessment, leading to better risk management and potentially more stable financial institutions.
Traditional actuarial models that are restrictive for irregular health data will be superseded by advanced AI/ML frameworks capable of handling nonlinearity and heterogeneity, leading to more precise financial products.
- · Insurance companies (disability, LTC)
- · Actuarial science
- · AI/ML developers
- · Healthcare data analytics
- · Providers of traditional actuarial software
- · Actuaries reliant on static models
More accurate disability insurance products and pricing will emerge.
This improved accuracy will lead to greater financial stability for insurers and potentially more accessible or fairer insurance for policyholders.
The success of AI in this domain could accelerate its adoption across other complex financial risk modeling, blurring lines between computational finance and healthcare AI.
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Read at arXiv cs.LG