scholarly journals Asymmetric Density for Risk Claim-Size Data: Prediction and Bimodal Data Applications

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2357
Author(s):  
Mansour Shrahili ◽  
Ibrahim Elbatal ◽  
Haitham M. Yousof
Keyword(s):  
Hazard Function ◽  
Claims Data ◽  
Risk Indicators ◽  
Insurance Companies ◽  
Claim Size ◽  
Insurance Claims ◽  
Asymmetric Data ◽  
Data Prediction ◽  
Different Types ◽  
Insurance Claims Data

A new, flexible claim-size Chen density is derived for modeling asymmetric data (negative and positive) with different types of kurtosis (leptokurtic, mesokurtic and platykurtic). The new function is used for modeling bimodal asymmetric medical data, water resource bimodal asymmetric data and asymmetric negatively skewed insurance-claims payment triangle data. The new density accommodates the “symmetric”, “unimodal right skewed”, “unimodal left skewed”, “bimodal right skewed” and “bimodal left skewed” densities. The new hazard function can be “decreasing–constant–increasing (bathtub)”, “monotonically increasing”, “upside down constant–increasing”, “monotonically decreasing”, “J shape” and “upside down”. Four risk indicators are analyzed under insurance-claims payment triangle data using the proposed distribution. Since the insurance-claims data are a quarterly time series, we analyzed them using the autoregressive regression model AR(1). Future insurance-claims forecasting is very important for insurance companies to avoid uncertainty about big losses that may be produced from future claims.

Risk assessment of building damage related to extreme winter windstorms in the canton of Zurich, Switzerland

2020 ◽  
Author(s):  
Thomas Röösli ◽  
Christoph Welker ◽  
David Bresch
Keyword(s):  
Risk Assessment ◽  
Claims Data ◽  
Rare Events ◽  
Damage Model ◽  
Building Damage ◽  
Insurance Companies ◽  
Wind Gust ◽  
Storm Damage ◽  
Insurance Claims ◽  
Insurance Claims Data

<p>We compare the risk assessment for storm related building damage based on three different foundations: (1) insurance claims data, (2) modelled building damages based on a historic event set of wind gust data, and (3) modelled building damages based on a probabilistic extension of the historic event set. Windstorms cause large socio-economic damages in Europe. In the canton of Zurich (Switzerland) they are responsible for one third of the building damages caused by natural hazards.</p><p>The Wind Storm Information Service (WISC) of the Copernicus Climate Change Service provides open wind gust datasets for the insurance sector to understand and assess the risk of windstorms in Europe. This is the first open climatological data set covering a longer time range than the insurance claims data of most small insurance companies. Our science-practice collaboration is a case study to illustrate how climatological data can be used in risk assessments in the insurance sector and how this approach compares to risk assessments based on proprietary claims data. We describe and use a storm damage model that combines wind gust data with exposure and vulnerability information to compute an event set of modelled building damages. These modelled damages are used to calculate relevant risk metrics for the insurance industry like the annual expected damage (AED) as well as the damage of rare events, with a return period of up to 250 years.</p><p>The AED calculated based on the insurance claims data (i.e. the mean damage over the observation period of 35 years) is 2.34 million Swiss Francs (CHF). This is almost double the value of the AED computed based on the storm damage model and historic event set (CHF 1.36 million). The storm Lothar/Martin in December 1999 is the most damaging event in the insurance claims data (CHF 62.4 million) as well as the historic event set (modelled building damage of CHF 62.7 million).</p><p>Both the insurance claims data and the modelled building damages based on historic events are not well suited to derive information about rare events with return periods considerably exceeding the observation period. To provide some information about rare events, we propose a new probabilistic event set, by introducing various perturbations, resulting in 4’200 events. This probabilistic event set results in an AED of CHF 1.45 million and a damage amount of CHF 75 million for a return period of 250 years. The probabilistic event set allows for testing the sensitivity of the risk to e.g. portfolio changes and changes in the insurance condition for events of a higher intensity than the historic events.</p><p>Our analysis is implemented in the GVZ’s proprietary storm damage model as well as the open-source risk assessment platform CLIMADA (https://github.com/CLIMADA-project/climada_python). This guarantees scientific reproducibility and offers insurance companies the opportunity to apply this methodology to their own portfolio with a low entry threshold.</p>

scholarly journals A Strategy to Identify Event Specific Hospitalizations in Large Health Claims Databases

2021 ◽  
Author(s):  
Joshua Lambert ◽  
Harpal Sandhu ◽  
Emily Kean ◽  
Teenu Xavier ◽  
Aviv Brokman ◽  
...  
Keyword(s):  
Health Insurance ◽  
Large Scale ◽  
Claims Data ◽  
Health Claims ◽  
Insurance Claims ◽  
Specific Event ◽  
Different Types ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims

Abstract Background Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to a specific event. This is because of the inherent disjointedness and lack of context that typically come with raw claims data. Methods In this paper, we propose a framework for classifying hospitalizations due to a specific event. Results We then test this framework in a health insurance claims database with approximately 4 million US adults who tested positive with COVID-19 between March and December 2020. Our claims specific COVID-19 related hospitalizations proportion is then compared to nationally reported rates from the Centers for Disease Control by age and sex. Conclusions The proposed methodology is a rigorous way to define event specific hospitalizations in claims data. This methodology can be extended to many different types of events and used on a variety of different types of claims databases.

scholarly journals Evaluating Hail Damage Using Property Insurance Claims Data

2015 ◽  
Vol 7 (3) ◽  
pp. 197-210 ◽  
Author(s):  
Tanya M. Brown ◽  
William H. Pogorzelski ◽  
Ian M. Giammanco
Keyword(s):  
Building Materials ◽  
Claims Data ◽  
Financial Burden ◽  
Insurance Companies ◽  
Insurance Claims ◽  
Building Product ◽  
Property Insurance ◽  
Damage Costs ◽  
Residential Properties ◽  
Insurance Claims Data

Abstract A series of thunderstorms on 24 May 2011 produced significant hail in the Dallas–Fort Worth (DFW) metroplex, resulting in an estimated $876.8 million (U.S. dollars) in insured losses to property and automobiles, according to the Texas Department of Insurance. Insurance claims and policy-in-force data were obtained from five insurance companies for more than 67 000 residential properties located in 20 ZIP codes. The methodology for selecting the 20 ZIP codes is described. This study evaluates roofing material type with regard to resiliency to hailstone impacts and relative damage costs associated with roofing systems versus wall systems. A comparison of Weather Surveillance Radar-1988 Doppler (WSR-88D) radar-estimated hail sizes and damage levels seen in the claims data is made. Recommendations for improved data collection and quality of insurance claims data, as well as guidance for future property insurance claims studies, are summarized. Studies such as these allow insurance underwriters and claims adjusters to better evaluate the relative performance and vulnerability of various roofing systems and other building components as a function of hail size. They also highlight the abilities and limitations of utilizing radar horizontal reflectivity-based hail sizes, local storm reports, and Storm Data for claims processing. Large studies of this kind may be able to provide guidance to consumers, designers, and contractors concerning building product selections for improved resiliency to hailstorms, and give a glimpse into how product performance varies with storm exposure. Reducing hail losses would reduce the financial burden on property owners and insurers and reduce the amount of building materials being disposed of after storms.

scholarly journals Direct and Indirect Costs of Asthma Burden in Abu Dhabi: A Retrospective Analysis of Insurance Claims Data from 2015 to 2018

2021 ◽  
Vol Volume 13 ◽  
pp. 969-980
Author(s):  
Khulood Al Mazrouei ◽  
Asma Ibrahim Almannaei ◽  
Faiza Medeni Nur ◽  
Nagham Bachnak ◽  
Ashraf Alzaabi
Keyword(s):  
Retrospective Analysis ◽  
Claims Data ◽  
Indirect Costs ◽  
Abu Dhabi ◽  
Insurance Claims ◽  
Direct And Indirect Costs ◽  
Insurance Claims Data

scholarly journals Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Claims Data ◽  
Health Care Spending ◽  
Insurance Claims ◽  
Diagnostic Coding ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims ◽  
Disease Specific

Abstract Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.

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