Search Results (106)

Non-life insurance pricing is forward-looking, yet its principal cost signal, reported claims, is delayed by the occurrence-to-reporting process. The rating cell is treated as a stylised, homogeneous unit without renewal, lapse, expiry, or cohort dynamics; a fully annual-contract formulation would require these dynamics to be modelled jointly with the risk and claims processes. This paper develops a partially observed risk-sensitive control framework in which premium-sensitive exposure generates claims in a latent risk regime, while unreported claims form an atomic population governed by an age-structured transport equation. The numerical instance solved and validated in this research restricts the general model to a memoryless (one-phase) reporting process for tractability. It is best matched to lines with predominantly short-to-medium reporting tails, rather than to the most extreme long-tailed liability or cyber exposures the general model is designed to eventually accommodate. A finite-state reporting reservoir and nominal Bayesian filter provide the decision state, and compound Poisson–Gamma loss enters an entropic Bellman recursion through a closed-form exponential-tilting identity, checked against an independently coded Bellman-residual test. Every dynamic policy is benchmarked against alternatives matched at the same risk-sensitivity parameter and evaluated using common random numbers. This is a general feature of the results, not a single statistic: $C{E}_{0.8}$ is a standardised yardstick applied uniformly across strategies, while $C{E}_{\gamma }$ at the policy’s own $\gamma$ is what that policy actually optimises, and the two need not agree. In 3000 out-of-model paths at $\gamma$ = 1.2, the delay-aware dynamic policy increases mean profit by EUR 0.148 million and a standardised ${\gamma }_{\mathrm{eval}}$ = 0.8 certainty equivalent by EUR 0.034 million relative to a matched static price. Its fifth percentile and TVaR 5%, by contrast, are lower by EUR 0.056 and 0.078 million. At the policy’s own optimisation level, however, the ${\gamma }_{\mathrm{eval}}$ = 1.2 certainty-equivalent difference is EUR −0.006 million, with a 95% interval reaching zero. The dynamic policy, therefore, does not clearly outperform the matched static price on the exact objective it was optimised to maximise. Matched comparisons attribute EUR 0.039–0.043 million of mean profit to reporting-delay modelling and EUR 0.017 million to dynamic continuation. Separating state observation from transition-law knowledge attributes EUR 0.161–0.182 million to observing the regime exactly, and a small, sign-changing EUR −0.010 to +0.003 million to knowing the true transition law itself. Across an eight-scenario misspecification stress suite, the dynamic policy’s mean-profit advantage over the matched static price is directionally robust in seven of eight scenarios, but it reverses sign under a +30% true-severity shock, indicating that this advantage is sensitive to substantial severity misspecification specifically. Grid, Bellman-residual, and out-of-model filter diagnostics indicate that state reconstruction is economically primary, while entropy risk sensitivity is a secondary overlay whose apparent benefit depends materially on which certainty-equivalent level and evaluation model are used to judge it. Full article

Extreme financial events are usually defined by loss magnitude alone, which quantifies severity but leaves open whether tail losses arrive in isolation or in temporally concentrated patterns. This paper develops a fractional tail-event timing diagnostic combining peaks-over-threshold (POT) generalized Pareto distribution (GPD) severity with Mittag-Leffler recurrence of interarrival times. We show that when exceedance times are driven by a stationary and ergodic latent intensity, the log-moment recurrence estimator has an explicit probability limit below the Poisson boundary that depends on the intensity only through its marginal log-variance, so volatility-driven dispersion of the arrival rate suffices to produce sub-Poisson estimates. In six daily series through 2026, the estimator is calibrated against a permutation null preserving the marginal loss distribution and the integer-gap discretization. That null sits near 1.05 rather than at unity, so the nominal Poisson boundary is not the correct reference for trading-day gaps. Against the calibrated null, the Deutscher Aktienindex (DAX), Nasdaq Composite, Nikkei 225, platinum, and the Standard & Poor’s 500 (S&P 500) display significant temporal concentration and coffee does not, whereas the monotone decline of the estimator across thresholds proves to be mechanical. Once goodness-of-fit tests are bootstrap-calibrated, no waiting-time law survives for four assets. The Mittag-Leffler parameter is therefore used only as an interpretable diagnostic index of temporal concentration; it is not a fitted-law claim and should not be interpreted as evidence of structural long memory or of a fractional data-generating mechanism. Full article

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction. Full article

Empirical evidence shows that the El Niño-Southern Oscillation (ENSO) influences the frequency–damage relationship for floods. However, ENSO is generally not incorporated into indemnity-trigger modeling of Flood Catastrophe Bonds (FCBs), resulting in an incomplete representation of claim events. Therefore, this study aims to develop an FCB pricing model that incorporates ENSO as an external systematic risk factor affecting the indemnity trigger. The trigger is formulated as a doubly stochastic compound Poisson process, with its intensity modeled as an autoregressive integrated moving-average with exogenous variables. Bond prices are then derived by integrating the trigger process with the Cox-Ingersoll-Ross model under an arbitrage-free risk-neutral framework. To obtain stable numerical solutions, a Monte Carlo-based algorithm is also developed. Numerical simulations using data from Bandung Regency, Indonesia, show stable estimates under the relative Monte Carlo standard error measure. Then, incorporating ENSO empirically improves flood-intensity forecasting accuracy, as indicated by lower MAPE, MAE, RMSE, and Theil’s U. It also produces statistically significant price differences across all common maturities. This study advances the theoretical and practical pricing of FCBs by directly linking climate-driven flood intensity to indemnity triggers, equipping practitioners to quantify risk better and to set sustainable disaster risk financing, particularly in ENSO-affected regions. Full article

Insurance pricing plays a central role in risk management and financial decision-making, as accurate premium estimation directly impacts portfolio stability and profitability. This study investigates insurance pure premium estimation by integrating classical actuarial models with modern machine learning techniques. We compare the traditional frequency–severity decomposition framework with direct modeling approaches, including XGBoost and Tweedie models. For claim frequency, we evaluate Poisson-based models, generalized additive models, and XGBoost. For claim severity, we compare a Gamma generalized linear model with XGBoost. The results show that XGBoost improves predictive performance for both components based on the evaluation metrics considered. Within the decomposition framework, the XGBoost–XGBoost model achieves the lowest prediction error among the models considered. However, lift-based analysis reveals that the XGBoost–Gamma model provides superior risk segmentation, highlighting a trade-off between prediction accuracy and risk ranking. Direct modeling approaches, while competitive, do not consistently achieve lower error than the decomposition framework across the evaluation metrics considered. Overall, the findings demonstrate that machine learning enhances predictive performance, but its effectiveness is maximized within the frequency–severity framework. The results highlight the importance of both frequency and severity modeling in insurance pricing, while suggesting that their relative contributions to risk segmentation depend on model specification and evaluation criteria. These findings have important implications for risk management and pricing strategies in insurance portfolios. Full article

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article

Work-related musculoskeletal disorders (MSDs) are leading causes of disability and productivity loss globally, yet registry-based evidence from low- and middle-income countries remains limited. The study analyzed compensated work-related MSDs claims reported to the Workers’ Compensation Fund (WCF) in Tanzania between 2016 and 2022 to identify patterns and associated risk factors. A registry-based cross-sectional design was conducted using de-identified WCF data on demographics, occupation, industry, diagnosis, and recorded workplace exposures. Modified Poisson regression was used to estimate associations between work-related MSDs and risk factors. Among the 243 workers with work-related MSDs whose claims were accepted and compensated, 84% had low back pain (LBP), predominantly males (90%) and middle-aged workers (mean age 41.6 years). Mining and quarrying accounted for 50% of the cases, with drivers and mobile plant operators being the most affected. Whole-body vibration (WBV) exposure and work in mining and quarrying were significant predictors of LBP (adjusted PR = 1.25; 95% CI: 1.061.49 and PR = 1.21; 95% CI: 1.01–1.44, respectively). These findings highlight WBV and mining work as significant risk factors of work-related MSDs and underscore the need for targeted interventions alongside enhanced health surveillance systems for exposure documentation. Full article

Background/Objectives: Understanding changes in dental service utilization is vital for planning effective oral health strategies in aging societies. In this study, we aimed to elucidate nationwide trends in major dental procedures in Japan from fiscal year (FY) 2016 to FY2023, and to assess the age, period, and cohort effects underlying these trends. Methods: Using open data from Japan’s National Database of Health Insurance Claims, five procedure types were analyzed: cavity filling, dental calculus removal, tooth extraction, dental crown procedures, and denture procedures. Descriptive analyses were performed to examine the annual and age-specific changes in the number of procedures per 1000 population. Age–period–cohort (APC) analyses were conducted using Poisson regression with spline functions, applying 10-year age groups. Results: From FY2016 to FY2023, restorative and prosthetic procedures, including cavity fillings, crowns, and dentures, demonstrated a steady decline, whereas preventive procedures, such as dental calculus removal increased, particularly among younger age groups. The APC analysis revealed distinct age-, period-, and cohort-related patterns in dental service utilization. Age effects indicated relatively higher rates of prosthetic procedures among older adults, whereas cohort effects suggested generational improvements in oral health. Period effects showed a downward shift beginning in FY2020, temporally aligned with the coronavirus disease pandemic. Conclusions: The combined descriptive and APC analyses indicate evolving patterns in dental service utilization in Japan, characterized by increased preventive care among younger generations and persistent age-related differences in prosthetic service use. These findings provide population-based evidence relevant for planning sustainable oral healthcare systems in aging societies. Full article

The New Polynomial Exponential Distribution (NPED), introduced by Beghriche et al. (2022), provides a flexible one-parameter family capable of representing diverse hazard shapes and heavy-tailed behavior. Regression frameworks based on the NPED, however, have not yet been established. This paper introduces two methodological extensions: (i) a generalized linear model (NPED-GLM) in which the distribution parameter depends on covariates, and (ii) a Poisson–NPED count regression model suitable for overdispersed and heavy-tailed count data. Likelihood-based inference, asymptotic properties, and simulation studies are developed to investigate the performance of the estimators. Applications to engineering failure-count data and insurance claim frequencies illustrate the advantages of the proposed models relative to classical Poisson, negative binomial, and Poisson–Lindley regressions. These developments substantially broaden the applicability of the NPED in actuarial science, reliability engineering, and applied statistics. Full article

Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such as Japan, South Africa, and Brazil. In Japan, telematics insurance has shown significant growth in recent years, with a steadily increasing subscription rate. In South Africa, UBI adoption ranks among the highest worldwide, with market penetration placing the country among the top three globally, just after the United States and Italy. In Brazil, UBI adoption is expanding, supported by government initiatives promoting road safety and innovation in the insurance sector. According to a MarketsandMarkets report of February 2025, the global UBI market is expected to grow from USD 43.38 billion in 2023 to USD 70.46 billion by 2030, with a compound annual growth rate (CAGR) of 7.2% over the forecast period. This growth is driven by the increasing adoption of both electric and internal combustion vehicles equipped with integrated telematics systems, which enable insurers to collect data on driving behavior and to tailor insurance premiums accordingly. In this paper, we analyze a large dataset consisting of trips recorded over five years from 100,000 policyholders across the Italian territory through the installation of black-box devices. Using univariate and multivariate statistical analyses, as well as Generalized Linear Models (GLMs) with Zero-Inflated Poisson distribution, we examine claims frequency and assess the relevance of various synthetic indicators of driving behavior, with the aim of identifying those that are most significant for insurance pricing. Full article

Each charging/discharging cycle leads to a gradual decrease in the battery’s capacity. The degradation of capacity in lithium-ion batteries represents a non-monotonous process with random jumps. Earlier studies claimed that the instantaneous degradation value of a lithium-ion battery is influenced by the historical dataset with long-range dependence. The existing methods ignore large jumps and long-range dependences in degradation processes. In order to capture long-range-dependent behavior with random jumps, we refer to the fractional Poisson process. We also outline the relationship between the long-range correlation and the Hurst index. The connection between random jumps in capacitance and long-range dependence of the fractional Poisson process is proven. In order to construct the fractional Poisson predictive model, we included fractional Brownian motion as the diffusion term and the fractional Poisson process as the jump term. The proposed approach is implemented on NASA’s dataset for Li-ion battery degradation. We believe that the error analysis for the fractional Poisson process is advantageous compared with that of the fractional Brownian motion, the fractional Levy stable motion, the Wiener model, and the long short-term memory model. Full article

In this paper, we introduce a generalised mutually exciting Hawkes process with random and independent jump intensities. This model provides a robust theoretical framework for modelling complex point processes and appropriately characterises the financial system, especially during periods of crisis. Based on this extended Hawkes process, we propose an insurance claim process and demonstrate that claim processes modelled as an aggregated process enable early detection of crises and inform optimal investment strategies in a financial system. Full article

Background: Japanese guidelines recommend the addition of a long-acting muscarinic antagonist for patients with asthma uncontrolled on inhaled corticosteroid/long-acting β₂-agonist (ICS/LABA) therapy, the effectiveness of which is evaluated here. Methods: Retrospective, observational, single-arm cohort study in patients with asthma initiating fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) following ICS/LABA, using independently analyzed data from Japanese claims databases: JMDC and Medical Data Vision (MDV). The index date was that of the first FF/UMEC/VI prescription. Outcomes were assessed during a 12-month follow-up versus a 12-month pre-index period (baseline) and included asthma exacerbations, oral corticosteroid (OCS) use, and short-acting β₂-agonist (SABA) use. P-values associated with rate ratios (RRs) were estimated using Conditional Poisson regression. Results: Overall, 3229 patients in the JMDC database and 1135 in the MDV database were included. Following FF/UMEC/VI initiation, the total annualized moderate–severe asthma exacerbation rate in the JMDC database reduced from 0.50 to 0.40 per-person-per-year (PPPY) (RR [95% confidence interval]: 0.78 [0.73, 0.84]; p < 0.001), with similar reductions in the MDV database: 0.53 to 0.42 PPPY (0.79 [0.70, 0.89]; p < 0.001). In both databases, there was a 20% reduction (JMDC: 0.80 [0.73, 0.88]; p < 0.001; MDV: 0.80 [0.68, 0.94]; p = 0.005) in patients with ≥1 OCS prescription after FF/UMEC/VI initiation. The proportion of patients with ≥1 SABA canister prescription dropped by 31% 0.69 [0.57, 0.84]; p < 0.001) in the JMDC database and 23% (0.77 [0.66, 0.90]; p < 0.001) in the MDV database. Conclusions: This suggests FF/UMEC/VI is effective in improving asthma exacerbations and reducing OCS and SABA use in Japanese patients previously using ICS/LABA in real-world clinical practice. Full article

In this paper, we study an extension of the classical compound Poisson risk model with a dependence structure among the inter-claim time and the subsequent claim size. Under a flexible dependence structure and assuming that the claim amounts are heavy tail distributed, we derive asymptotic tail moments for the aggregate claims. Numerical examples and simulation studies are provided to validate the results. Full article

Background: Existing research highlights the necessity of tailoring cost-containment policies to specific treatments due to the varying benefits across different diseases. This study contributes additional insights by examining the impact of such policies on a non-acute condition—cataracts. Methods: Leveraging 16 years of national health insurance claim data, this research assesses the influence of three prevalent cost-containment payment schemes on healthcare service utilization. Outcome variables for analysis include the decision to adopt intraocular lens (IOL) insertion, outpatient visit volume, and healthcare expenditures. The robustness of the findings is enhanced through the use of statistical methods, such as logit, Poisson, negative binomial, and panel fixed-effect models. Results: Global budgeting reduces the likelihood of procedure adoption and negatively impacts the volume of outpatient consultation services. Cost sharing does not affect procedure adoption but significantly impacts outpatient service volume. The prospective payment scheme for cataract IOL treatment shows no long-term effects on service utilization, with treatment rates stabilizing after a few years of policy implementation. Despite reimbursement points remaining unchanged for over two decades, there is no evidence of the under-provision of treatment. Conclusions: This study underscores the significant responsiveness of both patients and providers to policy reforms in the non-acute disease category. Manipulating payment schemes can lead to cost savings, particularly when treatment plans and procedures exhibit increased elasticity in their provision. Full article