A Transparent Computational Framework for Damage-State Exceedance Modelling Under Boundary Uncertainty: A Storm-Wind Insurance Case Study

Boundary uncertainty arises when a continuous insurance-based severity ratio is converted into ordered damage states using crisp thresholds, causing nearly identical claims to receive different labels solely because they fall on opposite sides of a decision boundary. This study presents a transparent computational framework that explicitly represents this uncertainty through predeclared piecewise-linear transition bands and soft labels while retaining probabilistic damage-state exceedance modelling and ordinal coherence within a transparent and auditable computational workflow. The empirical study uses 802 residential storm-wind insurance claims from Vojvodina, Serbia, recorded during 2013–2018. The response is the Relative Damage Ratio (RDR), calculated as the ratio of Insurance Claim Payout to policy Sum Insured and expressed as a percentage. The framework combines matched hard-label and soft-label cumulative logistic models with leakage-controlled calendar-month-blocked validation, paired blocked-bootstrap comparison and prespecified robustness analyses. Hard-label and soft-label models produced nearly identical proper-score and AUC point estimates; all paired 95% blocked-bootstrap confidence intervals for Brier-score, cross-entropy and calibration-slope differences included zero. Transition-width and local threshold-perturbation analyses likewise showed no consistent empirical advantage of either target representation. A secondary mathematically coupled specification incorporating log(Sum Insured), which also enters the denominator of RDR, yielded higher AUC values of approximately 0.76–0.80 compared with 0.58–0.63 in the primary wind-only models; these results are therefore interpreted as diagnostic rather than as evidence of independent physical predictive information. Rather than claiming predictive superiority for soft labels, the proposed framework makes boundary assumptions explicit while maintaining ordinal coherence by construction and yielding comparable calibration metrics, thereby providing a transparent, auditable and fully specified computational framework for ordinal probabilistic modelling in which continuous outcomes are converted into threshold-based cumulative states, with potential applicability to other ordinal risk-assessment settings.