Insurance Loss Research Articles

Application of flood catastrophe model to estimate revenue and insurance losses under unpredictable precipitation conditions

Application of flood catastrophe model to estimate revenue and insurance losses under unpredictable precipitation conditions

Microscopic traffic models, accidents, and insurance losses

AbstractThe paper develops a methodology to enable microscopic models of transportation systems to be accessible for a statistical study of traffic accidents. Our approach is intended to permit an understanding not only of historical losses but also of incidents that may occur in altered, potential future systems. Through such a counterfactual analysis, it is possible, from an insurance, but also from an engineering perspective, to assess the impact of changes in the design of vehicles and transport systems in terms of their impact on road safety and functionality.Structurally, we characterize the total loss distribution approximatively as a mean-variance mixture. This also yields valuation procedures that can be used instead of Monte Carlo simulation. Specifically, we construct an implementation based on the open-source traffic simulator SUMO and illustrate the potential of the approach in counterfactual case studies.

Application of Flood Catastrophe Model to Estimate Revenue and Insurance Losses under Unpredictable Precipitation Conditions

Application of Flood Catastrophe Model to Estimate Revenue and Insurance Losses under Unpredictable Precipitation Conditions

Transformative crop insurance solution with big earth data: Implementation for potato in India

ContextCrop insurance has become an indispensable risk management tool in the agricultural sector because globally crops are being exposed to multiple hazards. The lack of reliable crop yield data has impacted the sustenance of area-yield crop insurance schemes. Index-based insurance, which links pay-outs to crop performance proxies rather than measured losses, is being explored to improve the effectiveness of crop insurance contracts. ObjectiveThis paper presents an innovative crop insurance scheme that has replaced the existing ‘area-yield’ approach using bias-prone crop yield estimates with the ‘area-crop performance approach’ using objectively measured satellite indices. MethodSatellite-based crop mapping, satellite and weather-based crop health indicators, field data collection and analysis, composite index generation, and insurance loss assessment are major tasks in the project. Data of Sentinel-1 and 2 satellites, weather datasets and mobile app-based field data from transplantation to harvesting of the crop constituted a huge repository of the database in this project. Metrics derived from established satellite indices, such as NDVI, LSWI and Backscatter, along with weather indices, were synthesized into a composite index of crop performance called Crop Health Factor (CHF). The input data matrix of the CHF model included eight input indicators. After data normalization, weights for these indicators were generated using the entropy technique, a proven method of information measurement that produces balanced relationships and unbiased weights. The CHF was first generated with data from the past years (2016–2019), and the resulting weights were then applied to the normalized data of the current year (2020). ResultsThe current crop insurance scheme, using CHF data instead of yield data, was implemented in the state of West Bengal, India, covering about 500,000 ha of potato across and one thousand insurance units in the 2020 crop season. The CHF and yield data from past years showed similar patterns in the majority of cases. The indemnity level was set at 70 % of the normal CHF, which was the average CHF of past years. Loss assessment and compensation payouts for the current year were determined by the extent of CHF reduction beyond the indemnity level. SignificanceThis new index-insurance scheme has many advantages over the conventional yield-based scheme in terms of transparency, objectivity and ease of implementation. There is scope for improving the composite index with additional features. Such technology-driven index-insurance schemes for field crops are expected to bring a paradigm shift in the crop insurance sector, giving rise to new business models and benefitting all the stakeholders.

The new Topp-Leone exponentied exponential model for modeling financial data

<abstract><p>We proposed in this article a new three-parameter distribution, which is referred as the Topp-Leone exponentiated exponential model is proposed. It is used in modeling claim and risk data applied in actuarial and insurance studies. The probability density function of the suggested distribution can be unimodel and positively skewed. Different distributional and mathematical properties of the TL-EE model were provided. Furthermore, we established a maximum likelihood estimation method for estimating the unknown parameters involved in the model, and some actuarial measures were calculated. Also, the potential of these actuarial statistics were provided via numerical simulation experiments. Finally, two real datasets of insurance losses were analyzed to prove the performance and superiority of the suggested model among all its competitors distributions.</p></abstract>

The impact of COVID-19 on healthcare coverage and access in racial and ethnic minority populations in the United States.

This study described spatiotemporal changes in health insurance coverage, healthcare access, and reasons for non-insurance among racial/ethnic minority populations in the United States during the COVID-19 pandemic using four national survey datasets. Getis-Ord Gi* statistic and scan statistics were used to analyze geospatial clusters of health insurance coverage by race/ethnicity. Logistic regression was used to estimate odds of reporting inability to access healthcare across two pandemic time periods by race/ethnicity. Racial/ethnic differences in insurance were observed from 2010 through 2019, with the lowest rates being among Hispanic/Latino, African American, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander populations. Pre-pandemic insurance coverage rates were geographically clustered. The percentage of adults citing change in employment status as the reason for non-insurance increased by about 7% after the start of the pandemic, with a small decrease observed among African American adults. Almost half of adults reported reduced healthcare access in June 2020, with 38.7% attributing reduced access to the pandemic; however, by May 2021, the percent of respondents reporting reduced access for any reason and due to the pandemic fell to 26.9% and 12.7%, respectively. In general, racial/ethnic disparities in health insurance coverage and healthcare access worsened during the pandemic. Although coverage and access improved over time, pre-COVID disparities persisted with African American and Hispanic/Latino populations being the most affected by insurance loss and reduced healthcare access. Cost, unemployment, and eligibility drove non-insurance before and during the pandemic.

Risk contribution to deposit insurance: Evidence from commercial and cooperative banks in the Eurozone

This paper analyses how the risk of commercial and cooperative banks may affect the European Deposit Guarantee Scheme (EDIS) to assess the appropriateness of a differentiated contribution regime. Evidence shows that these institutions have different objectives, ownership structures and operational approaches that impact their risk profile. As a result, a single regulatory approach may not be appropriate and may place an additional burden on cooperative banks. Using a simulation model and a sample of banks representing 81% of deposits in the Eurozone between 2018 and 2021, we analyse risk, deposit insurance losses and financial coverage needs. We find that cooperative banks are less risky, generate fewer losses and should provide fewer resources compared to commercial banks. These findings are important to achieve a more equitable and efficient funding policy in the future development of the EDIS as the third pillar to complete the Banking Union.

A New Inverse Extended Weibull Distribution for Modelling Insurance Loss Data

This paper proposes a new three-parameter inverse Weibull-type distribution via a similar way of constructing a skew normal distribution. The additional shape parameter increases the distribution’s modelling capability as other three-parameter inverse Weibull-type distributions. The mathematical properties of this inverse extended Weibull distribution are studied and proven. In the empirical study of four insurance datasets from Australia, Denmark and the USA, we show that the proposed distribution performs very well compared with other three-parameter competitors.

Scenario-Based Seismic Risk Assessment for the Reykjavik Capital Area

About two-thirds of the population in Iceland lives in the Reykjavik capital area (RCA), which is close to active volcanoes and seismic zones. In the period 1900–2019, a total of 53 earthquakes of Mw≥5.0 struck in these zones. The two largest events in the Reykjanes Peninsula, Mw6.36 and Mw6.12, occurred in 1929 and 1968, respectively. Both events were less than 20 km from the outskirts of the RCA. Late in the year 2020, the seismicity on the peninsula greatly increased due to magma intrusion and volcanic activity, which has so far resulted in three eruptions, in 2021, 2022, and 2023, and six earthquakes of Mw≥5.0. Based on historical and geological data, the ongoing activity is probably the initial phase of an active period ahead that could continue for many decades, and has the potential to trigger larger earthquakes like those in 1929 and 1968. Further east, in the South Iceland Seismic Zone, two earthquakes of Mw6.52 and 6.44 struck in June 2000, and in May 2008, a Mw6.31 earthquake occurred. In both cases, around 5000 buildings were affected. Insurance loss data from these events have been used to develop empirical vulnerability models for low-rise buildings. In this study, the loss data are used to calibrate seismic vulnerability models in terms of the source-site distance. For a given magnitude scenario, this provides a simpler representation of seismic vulnerability and is useful for emergency planning and disaster management. These models are also used to compute different types of scenario risk maps for the RCA for a repeat of the 1929 earthquake.

Exploring Delivery of Adolescent Preventive Health Services During the COVID-19 Pandemic: Perceptions and Experiences Shared by Healthcare Professionals

Abstract Background While unmitigated vaccine hesitancy was already growing before the emergence of COVID-19, the global pandemic has introduced additional challenges to the immunization sector. Despite the benefits, routine preventive care was forestalled by challenges related to pandemic lockdowns/quarantine, parental reluctance to seek preventative services, poor access to healthcare, loss of income and/or health insurance, and the inability of children under 12 to be immunized against the coronavirus. Considering the potential impacts caused by the pandemic’s disruption of adolescent preventative care services, we explored the experiences of adolescent healthcare providers in Tennessee and Georgia in providing care throughout the COVID-19 pandemic. Methods A series of in-depth qualitative interviews were conducted between December 2020 and May 2021 via the video conferencing platform, Zoom, among a diverse group of adolescent healthcare providers (n=16) representing 5 healthcare practices located within the southeastern states of Georgia and Tennessee. Audio recordings were transcribed verbatim and analyzed using a rapid qualitative analysis framework, guided by the grounded theory and inductive approach. Results Preventive adolescent services were adversely impacted by the COVID-19 pandemic at all participating practices. Participants highlighted an initial decrease in patients due to the pandemic, despite the implementation of protective measures such as an increase in sanitation measures, hand washing, separation of sick and wellness visits, mask mandates, and telehealth/video conferencing opportunities. While practices continued to distribute educational materials, many removed material from their waiting rooms due to sanitary concerns related to COVID-19. Participants reported that patient-provider communications; effective use of presumptive languaging; periodic reminders/recall messages; provider’s personal recommendation on vaccine safety/efficacy; early initiation of human papillomavirus (HPV) vaccination series at 9 years; community partnerships with health navigators/vaccine champions/vaccine advocates; use of standardized forms/prewritten scripts/standard operating protocols for patient-provider interactions; and vaccine promotion through social media, brochures/posters/pamphlets, as well as outreach to schools and churches, served as facilitators to adolescent HPV vaccine uptake. Conclusions By exploring the perceptions and experiences of adolescent healthcare providers, our research identified key factors influencing adolescent vaccination uptake and its relatedness to the COVID-19 pandemic. We argue our findings support and encourage the development of personalized patient-provider communications, trusting relationships, effective use of presumptive languaging, and counteraction of misinformation as solutions to suboptimal preventative services and vaccination coverage rates. Implementation of these comprehensive strategies could not only ameliorate routine adolescent vaccination uptake but could also aid in the expansion of COVID-19 vaccine acceptance on a sizable scale.

Automatic detection of crop lodging from multitemporal satellite data based on the isolation forest algorithm

Rapid identification of crop lodging has implications for optimizing disaster prevention and mitigation measures. This study proposes a sequentially integrated algorithm for crop lodging identification, which first automatically extracts lodged and non-lodged samples based on the isolation forest algorithm from the screened features dataset and then uses a supervised classifier for lodging identification. Taking the crop lodging disaster caused by the super typhoon “Maysak” in Bohetai Township as an example, we calculated 162 feature indices from Sentinel-1 and Sentinel-2 satellite data pre- and post-disaster and screened the optimal features. Then, the abnormal scores of the cultivated-land pixels were calculated using the isolation forest algorithm. After that, we calculated the coefficient of variation (CV) of the abnormal scores of each group, extracted the lodged and non-lodged samples based on the cumulative CV contribution rate, and input samples into three supervised classifiers (random forest, maximum likelihood, and support vector machine) to identify lodged crop pixels. The results showed that feature screening increased the identification accuracy of crop lodging by more than 10% and difference features of short-wave infrared (SWIR) and red edge bands contributed significantly to the identification. The accuracy of crop lodging automatic identification was verified using samples obtained from visual interpretation and digitization of UAV images as reference data and the overall accuracy reached 78%. The proposed method can simultaneously recognize lodging of different crop types without the need to select lodged samples and extract lodged ranges for each crop. With low data acquisition costs and high automation, the proposed method has promising applications in agricultural disaster emergency management and agricultural insurance loss assessment.

“I felt super lucky:” Luck and administrative burden in cancer-related financial hardship.

198 Background: Financial hardship after cancer diagnosis is common and leads to earlier mortality and worse quality of life. This qualitative study sought to identify causal and protective factors for financial hardship as part of a larger study to refine a theoretical model and measure of financial hardship. Methods: We recruited female breast cancer survivors at a large comprehensive cancer center. Participants completed a semi-structured interview focused on their reflections of cancer diagnosis and active treatment. Interviews assessed multiple dimensions of financial hardship (coping, impacts, worry/anxiety, depression) and contributing, protective, and contextual factors. We conducted framework qualitative analysis of interview transcripts. Results: We interviewed eighteen women with breast cancer (100% early or regional stage; 77% White; mean age 57; time since diagnosis 3-24 years). Participants reported varying levels of financial hardship during diagnosis and treatment. Financial coping behaviors ranged from minor rebudgeting, to skipping or delaying healthcare, to major decisions about family structure (e.g. having children; marriage or divorce). Even successful financial coping induced stress (“it was just really painful”) because of administrative and time burden: hypervigilance to bills and finances; waiting in phone queues; negotiating with insurance companies and employers; liaising between employer, insurance company, and care team; and learning about, gathering documents, and applying for benefits (e.g. FMLA; disability). Employment and generous health insurance were protective, but out-of-network care and coverage denials tempered this protection and introduced stress. Worry about employment or insurance loss and the administrative burdens required to financially cope also created stress. Other protective factors were largely described as good luck, such as understanding bosses, flexible work arrangements, multiple household incomes, assistance from family and friends, or avoidance of more severe hardship (“we were fortunate we didn't lose our home"). Easily accessible health insurance was also described as good luck. Conclusions: Administrative and time burden required for financial coping behaviors can induce distress for women with breast cancer, even if successful in mitigating financial hardship. Luck emerged as a prominent protective factor. Luck and its relationship to structural factors that generate inequity may have ethical and policy implications for addressing cancer-related financial hardship and is worthy of further study.

Model Uncertainty and Selection of Risk Models for Left-Truncated and Right-Censored Loss Data

Insurance loss data are usually in the form of left-truncation and right-censoring due to deductibles and policy limits, respectively. This paper investigates the model uncertainty and selection procedure when various parametric models are constructed to accommodate such left-truncated and right-censored data. The joint asymptotic properties of the estimators have been established using the Delta method along with Maximum Likelihood Estimation when the model is specified. We conduct the simulation studies using Fisk, Lognormal, Lomax, Paralogistic, and Weibull distributions with various proportions of loss data below deductibles and above policy limits. A variety of graphic tools, hypothesis tests, and penalized likelihood criteria are employed to validate the models, and their performances on the model selection are evaluated through the probability of each parent distribution being correctly selected. The effectiveness of each tool on model selection is also illustrated using well-studied data that represent Wisconsin property losses in the United States from 2007 to 2010.

Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be “thinner” than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.

Climatic damage cause variations of agricultural insurance loss for the Pacific Northwest region of the United States.

Abstract Agricultural crop insurance is an important component for mitigating farm risk, particularly given the potential for unexpected climatic events. Using a 2.8 million nationwide insurance claim dataset from the United States Department of Agriculture (USDA), this research study examines spatiotemporal variations of over 31,000 agricultural insurance loss claims across the 24-county region of the inland Pacific Northwest (iPNW) portion of the United States, from 2001 to 2022. Wheat is the dominant insurance loss crop for the region, accounting for over 2.8 billion dollars in indemnities, with over 1.5 billion dollars resulting in claims due to drought (across the 22 year time period). While fruit production generates considerably lesser insurance losses (400 million dollars) as a primary result of freeze, frost and hail, overall revenue ranks number one for the region, with 2 billion dollars in sales, across the same time range. Principal components analysis of crop insurance claims showed distinct spatial and temporal differentiation in wheat and apples insurance losses using the range of damage causes as factor loadings. The first two factor loadings for wheat accounts for approximately 50 percent of total variance for the region, while a separate analysis of apples accounts for over 60 percent of total variance. These distinct orthogonal differences in losses by year and commodity in relationship to damage causes suggest that insurance loss analysis may serve as an effective barometer in gauging climatic influences.

Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India

Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers.

Cover crops, crop insurance losses, and resilience to extreme weather events

AbstractThis study investigates whether cover crop adoption reduces extreme‐weather‐related crop insurance losses. To achieve this objective, we utilize a county‐level panel data set with information on cover crop adoption acres, crop insurance losses (i.e., specifically due to drought, excess heat, or excess moisture), and a number of weather variables. The data cover the main row crop production region in the Midwestern United States (US) for the period 2005 to 2018. We utilize linear fixed effects econometric models and a number of robustness checks in the empirical analysis (i.e., a fractional regression approach, two “external‐instrument‐free” estimation procedures, and a variety of alternative empirical specifications). The estimation methods used take advantage of the panel nature of the data to address various specification and endogeneity issues. We find evidence that counties with higher cover crop adoption tend to have lower crop insurance losses due to drought, excess heat, or excess moisture. Our analysis also indicates that cover crops likely have stronger loss mitigation effects against excess moisture events (like floods) and somewhat weaker loss mitigation impacts against droughts and excess heat. Nonetheless, our results overall suggest that cover crops can enhance resilience to extreme weather events and have the potential to be an effective climate change adaptation strategy in US agriculture.

Dirichlet Process Log Skew-Normal Mixture with a Missing-at-Random-Covariate in Insurance Claim Analysis

In actuarial practice, the modeling of total losses tied to a certain policy is a nontrivial task due to complex distributional features. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to eliminate the risk of model misspecification biases. However, the effect of covariates as well as missing covariates in the modeling framework is rarely studied. In this article, we propose novel connections among a covariate-dependent Dirichlet process mixture, log-normal convolution, and missing covariate imputation. As a generative approach, our framework models the joint of outcome and covariates, which allows us to impute missing covariates under the assumption of missingness at random. The performance is assessed by applying our model to several insurance datasets of varying size and data missingness from the literature, and the empirical results demonstrate the benefit of our model compared with the existing actuarial models, such as the Tweedie-based generalized linear model, generalized additive model, or multivariate adaptive regression spline.

Mathematical Model of Paddy Production using Cobb Douglas Method Based On Weather Factors

This research was conducted to model paddy production based on weather factors. This needs to be done to predict crop yields and regulate paddy cropping patterns. In setting the cropping pattern, the weather is selected which consists of temperature, wind speed, and rainfall, as a variable factor of production. Meanwhile, other factors (such as fertilization, sunshine, air humidity, etc.) are assumed to be in catteries paribus conditions. The research method used is a mixed method between qualitative methods which are descriptive details and quantitative methods which are based on weather data and Paddy's harvest data. The aim of this research is to analyze the influence of weather on paddy production results. Analysis is done to get the production function. Parameters are estimated using the Ordinary Least Square (OLS) method by minimizing the sum of squared errors. Based on data analysis, a correlation of 0.899 was obtained with a standard error of .051665515. the results of model testing also show significant results with the F statistic obtained at 33.98 with a p-value of 0.028 which is less than 5%. So it can be concluded that there is a significant relationship between weather and paddy productivity. In such a way that the weather can be used as a reference in determining the prediction of loss risk and paddy production. This model can also be recommended for further research, namely to determine insurance losses that may arise when extreme weather events occur.

Equilibrium reporting strategy: Two rate classes and full insurance

AbstractWe propose a multiperiod insurance model under a bonus–malus system with two rate classes and consider an insured who has purchased full insurance for her losses. To explore the potential advantage of underreporting her insurable losses, the insured follows a barrier strategy and only reports lossses above the barrier to the insurer. We obtain a unique equilibrium declaration strategy in closed form for a risk‐neutral insured who maximizes her expected wealth, and in semiclosed form for a risk‐averse insured who maximizes her expected exponential utility of wealth, both over an exogenous random horizon. We find that the equilibrium barriers for the two classes are equal and strictly greater than zero, offering a theoretical explanation for the underreporting of insurable losses, a form of ex post moral hazard. Finally, we consider the case of three rate classes and show, through numerical examples, that the equilibrium barriers are not equal.