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151 results for “insurance”
Health Insurance Claims
<p>The dataset is eligible in exploring Health Insurance fraud Claims using machine learning algorithms. Its well suited for students developimg ML models to predict Healthcare insurance claims fraud. </p> <p> </p>
Benchmark and training data for replicating financial and insurance examples
<p>This dataset contains training, validation and out-of-sample test data for two European calls and two examples of portfolio of variable annuity guarantees.</p>
Supplementary materials for the paper "Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach"
<p>These are materials necessary to replicate the study discussed in <em>Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach</em>, accepted for publication at VEHITS 2022.</p>
H2020 CYBECO WP6 – Behavioural-Experimental Analysis of Cyber Insurance Tools
<p>These files contain the datasets collected in the Experiment 1 and Experiment 2 of the EU H2020 project CYBECO (grant no 740920). Their codebooks present the description of all the variables contained in the datasets as well as their rationales and the screenshots of the experimental software. Deliverable 6.3: Report with Findings of Experiments and Policy implications<a href="#_ftn1">[1]</a> of CYBECO project presents the details of the implementation of both experiments, the results obtained and their implications for the validation and potential improvement of CYBECO model and toolbox.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> <a href="https://www.cybeco.eu/results">https://www.cybeco.eu/results</a></p>
Smart Insurance datasets subset of the AEGIS project
<p>The example dataset was produced within the Smart Insurance demonstrator of the AEGIS project. It contains a sample of the SYNTHETIC data that have been created for the demonstrator purposes. </p>
Riverine Flood Insurance assessment indicators under climate and socio-economic change
<p>Expected annual river flood damages, flood insurance premiums, and insurance penetration rates, for EU-regions (NUTS2) and under future climatic and socio-economic conditions (RCP-SSP combinations).</p>
Data from: Climatic damage cause variations of agricultural insurance loss for the Pacific Northwest region of the United States
<p>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 apple 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.</p>
National Flood Insurance Program Community Rating System Net Load
<p>The Community Rating System in the National Flood Insurance Program provides discounts to policyholders. However, the discount is cross-subsidized within each state meaning that some policyholders are paying higher insurance premiums that what is actuarially sound and are subsidizing the policies of others. This dataset shows how much the average policy in a county is benefiting from the cross-subsidization (negative net CRS load) or paying extra (positive net CRS load).</p> <p>All datasets were generated using the code in the <a href="https://github.com/ddusseau/NetCRSLoad">NetCRSLoad Github repository</a>. </p> <p> </p> <p>File descriptions are below:</p> <p><em>NFIP_crs_2025-01-01.csv</em> - The processed National Flood Insurance Program policies dataset.</p> <p><em>HE_Rural_Capacity_Index_March_2024_Download_Data.csv</em> - Headwater Economics Rural Capacity Index dataset. https://headwaterseconomics.org/equity/rural-capacity-map/. </p> <p><em>fema_risk-rating-2.0_exhibits-2-3-4.xlsx</em> - National Flood Insurance Program Risk Rating 2.0 Single-Family Homes policies data. https://www.fema.gov/flood-insurance/work-with-nfip/risk-rating/single-family-home. </p> <p><em>state_fips_master.csv</em> - FIPS codes for states.</p> <p><em>cb_2018_us_county_20m_netCRSload_2025-01-01.shp</em> - Community Rating System net load averaged at the county level for NFIP policies in force on January 31, 2024. The "net_crs_lo" field represents the net load in dollar amounts. The "crsLoadPct" field represents the net load as a percent.</p> <p><em>cb_2018_us_county_20m_netCRSload_RR2.shp</em> - Community Rating System net load averaged at the county level for NFIP single-family home policies under Risk Rating 2.0. The "net_crs_lo" field represents the net load in dollar amounts. The "crsLoadPct" field represents the net load as a percent.</p> <p><em>cb_2018_us_county_20m.shp</em> - The county geospatial boundaries from the US Census. https://www.census.gov/geographies/reference-files/2020/demo/popest/2020-fips.html. </p>
When Less is More: Improving Choices in Health Insurance Markets
<p>We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrollment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modeling reveals large ``choice inconsistencies'' such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of ``approximate inertia'' where enrollees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrollees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or ``choice overload''. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization. </p>
Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
<p>We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a R<sup>2</sup> of 0.49 and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.</p>
Dataset: American Coastal Insurance Corporation (ACIC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Goosehead Insurance, Inc (GSHD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: The Baldwin Insurance Group, Inc. (BWIN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: American Coastal Insurance Corporation (ACIC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Skyward Specialty Insurance Group, Inc. (SKWD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Selective Insurance Group, Inc. (SIGIP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Selective Insurance Group, Inc. (SIGI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Safety Insurance Group, Inc. (SAFT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco KBW Property & Casualty Insurance ETF (KBWP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: International General Insurance Holdings Ltd. (IGIC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.