Oregon workers' compensation claims counts. Where available, the data is provided since 1968, the year Oregon's modern workers' compensation system began. The data is presented in the Department of Consumer and Business Services report at https://www.oregon.gov/dcbs/reports/compensation/Pages/index.aspx. The attached pdf provides definitions of the data.

Losses caused by lightning in the United States were the cause behind a total of 70,787 insurance claims paid by homeowner insurance companies in 2023. In 2008, lightning caused around 246,000 homeowner insurance claims in the same country.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-03-15 about initial claims, headline figure, and USA.

Data set listing the individual claims filed against the City of New York and the individual claims settled by the City of New York during the prior fiscal year.

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.

In 2022, there were more than eight million auto insurance claims submitted in Germany. The largest share was for comprehensive, or Vollkasko, insurance, which accounted for 4.1 million claims, followed by third-party liability with 3.36 million claims.

Congress passed the National Flood Insurance Act (NFIA), 42 U.S.C. 4001 in 1968, creating the National Flood Insurance Program (NFIP) in order to reduce future flood losses through flood hazard identification, manage floodplain, and provide insurance protection. The Department of Housing and Urban Development (HUD) originally administered the NFIP, and Congress subsequently transferred the NFIP to FEMA upon its creation in 1979. FEMA and insurance companies participating in FEMA's Write Your Own (WYO) program offer NFIP insurance coverage for building structures as well as for contents and personal property within the building structures, to eligible and insurable properties. The WYO program began in 1983 with NFIP operating under Part B of the NFIA and allows FEMA to authorize private insurance companies to issue the Standard Flood Insurance Policy (SFIP) as FEMA's fiduciary and fiscal agent. FEMA administers NFIP by ensuring insurance applications are processed properly; determining correct premiums; renewing, reforming, and cancelling insurance policies; transferring policies from the seller of the property to the purchaser of the property in certain circumstances; and processing insurance claims. rnrnThe paid premiums of SFIPs and claims payments for damaged property are processed through the National Flood Insurance Fund (NFIF). NFIF was established by the National Flood Insurance Act of 1968 (42 U.S.C. 4001, et seq.), and is a centralized premium revenue and fee-generated fund that supports NFIP, which holds these U.S. Treasury funds. rnrnThis dataset is derived from the NFIP system of record, staged in the NFIP reporting platform and redacted to protect policy holder personally identifiable information.rnrnThe NFIP Transactional Record Reporting Process (TRRP) Plan (https://nfipservices.floodsmart.gov/manuals/jan_2015_consolidated_trrp.pdf ) defines for the WYO companies how to report policy and claims information to the NFIP. The Flood Insurance Manual (https://nfipservices.floodsmart.gov/home/manuals ) establishes how claims should be adjusted. The NFIP has provided answers to Frequently Asked Questions (FAQs) to assist the public in understanding and navigating the data our program makes available: https://www.fema.gov/sites/default/files/documents/fema_nfip-data-faqs.pdfrnrnThis dataset represents more than 2,000,000 claims transactions, in order to improve accessibility, we have one compressed file. Due to the file size we recommend using Access, SQL, or another programming/data management tool to visualize and manipulate the data, as Excel will not be able to process files this large without data loss. The dataset will be updated approximately monthly and will have a lag with the system of record. rn rnThis dataset is not intended to be an official federal report and should not be considered an official federal report. rn rnCitation: The Agency's preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

This dataset reports total weekly unemployment insurance initial claims and continued weeks claimed statewide in Iowa by week. Data for the most current week is preliminary.

Initial claims data for states are combined and published weekly by the U.S. Department of Labor, Employment and Training Administration. This national data is widely reported as an economic indicator. This data is based on the ETA-539 report.

This dataset is based on administrative data. Claims activity represents the week the claims were processed. It may not always represent the week unemployment occurred.

This is a peer-reviewed supplementary table for the article 'Healthcare resource utilization, costs and treatment associated with myasthenia gravis exacerbations among patients with myasthenia gravis in the USA: a retrospective analysis of claims data' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: MG treatment definitionsAim: There are limited data on the clinical and economic burden of exacerbations in patients with myasthenia gravis (MG). We assessed patient clinical characteristics, treatments and healthcare resource utilization (HCRU) associated with MG exacerbation. Patients & methods: This was a retrospective analysis of adult patients with MG identified by commercial, Medicare or Medicaid insurance claims from the IBM MarketScan database. Eligible patients had two or more MG diagnosis codes, without evidence of exacerbation or crisis in the baseline period (12 months prior to index [first eligible MG diagnosis]). Clinical characteristics were evaluated at baseline and 12 weeks before each exacerbation. Number of exacerbations, MG treatments and HCRU costs associated with exacerbation were described during a 2-year follow-up period. Results: Among 9352 prevalent MG patients, 34.4% (n = 3218) experienced ≥1 exacerbation after index: commercial, 53.0% (n = 1706); Medicare, 39.4% (n = 1269); and Medicaid, 7.6% (n = 243). During follow-up, the mean (standard deviation) number of exacerbations per commercial and Medicare patient was 3.7 (7.0) and 2.7 (4.1), respectively. At least two exacerbations were experienced by approximately half of commercial and Medicare patients with ≥1 exacerbation. Mean total MGrelated healthcare costs per exacerbation ranged from $26,078 to $51,120, and from $19,903 to $49,967 for commercial and Medicare patients, respectively. AChEI use decreased in patients with multiple exacerbations, while intravenous immunoglobulin use increased with multiple exacerbations. Conclusion: Despite utilization of current treatments for MG,MG exacerbations are associated with a high clinical and economic burden in both commercial and Medicare patients. Additional treatment options and improved disease management may help to reduce exacerbations and disease burden.

The frequency of private passenger comprehensive auto insurance claims for physical damage in the United States rose to 3.15 per 100 car years in 2021, compared to 2.7 in 2020. This was the highest frequency recorded over the past 15 years.

Jobless Claims 4-week Average in the United States decreased to 224 Thousand in March 22 from 228.75 Thousand in the previous week. This dataset provides - United States Jobless Claims 4-week Average- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Explore the Claims Processing Software Global Market Report 2025 Market trends! Covers key players, growth rate 9.7% CAGR, market size $66.23 Billion, and forecasts to 2033. Get insights now!

Problem Statement

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An insurance company faced significant inefficiencies in its claims processing operations. The manual review and assessment of claims were time-consuming, prone to errors, and resulted in delays that frustrated customers. The company needed a solution to streamline claims processing, reduce operational costs, and improve customer satisfaction.

Challenge

Automating insurance claims processing involved addressing several challenges:

Handling diverse claim types, including structured and unstructured data such as invoices, photographs, and customer narratives.

Ensuring accurate claims assessment while detecting potential fraud.

Integrating automation with existing systems without disrupting ongoing operations.

Solution Provided

An AI-powered claims processing system was developed using machine learning and workflow automation technologies. The solution was designed to:

Extract and validate data from claim submissions automatically.

Assess claims using predictive models to estimate coverage and liability.

Flag potential fraudulent claims for further investigation.

Development Steps

Data Collection

Collected historical claims data, including structured data from forms and unstructured data such as photos and handwritten notes, to train machine learning models.

Preprocessing

Standardized and cleaned data, ensuring compatibility across various sources. Applied optical character recognition (OCR) for extracting data from scanned documents.

Model Development

Developed machine learning models to evaluate claims based on historical trends and patterns. Built fraud detection algorithms to identify anomalies in claims data.

Validation

Tested the system with live claims data to ensure accuracy in assessment, fraud detection, and operational efficiency.

Deployment

Implemented the solution across the company’s claims processing system, enabling seamless operation and real-time processing.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and workflows based on new data and user feedback.

Results

Accelerated Claims Processing Time

The automation system reduced claims processing time by 60%, enabling quicker payouts and enhancing customer satisfaction.

Reduced Operational Costs

Automating routine tasks lowered operational costs by minimizing manual labor and administrative overhead.

Improved Customer Satisfaction

Faster and more accurate claims processing improved customer experience and strengthened trust in the company’s services.

Enhanced Fraud Detection

The system’s predictive algorithms flagged suspicious claims effectively, reducing the risk of fraudulent payouts.

Scalable and Adaptive Solution

The solution scaled seamlessly to handle increased claim volumes, ensuring consistent performance during peak periods.

Initial Jobless Claims in the United States increased to 223 thousand in the week ending March 15 of 2025 from 221 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Between 2017 and 2019, the European country with the highest volume of health insurance claims was the Netherlands. Health insurers paid out almost 49.4 billion euros in 2019 alone, whereas German health insurers paid out 29.8 billion euros in the same year. Insurance density was higher in the Netherlands than in Germany in the same year.

The payments for life insurance claims by the Finnish company OP Life Assurance peaked in 2019 before falling in subsequent years. In 2022, the Finnish insurer paid out 1.16 billion euros to their customer for life insurance losses incurred.

The Division of Workers’ Compensation (DWC), Workers’ Compensation Information System (WCIS) has been collecting First Reports of Injury (FROI) and Subsequent Reports of Injury (SROI) submitted electronically by claims administrators and their trading partners using the International Association of Industrial Accident Boards and Commissions (IAIABC) FROI/SROI Release 1.0 standard since 2000. The numbers reflect the WCIS database information as of the run date of the report.

This dataset contains mining claim cases with the case disposition (status) of anything other than closed from US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS). The BLM only requires that mining claims be identified down to the affected quarter section(s)—as such, that is what the MLRS research map and public reports will reflect, most commonly. Claim boundaries, as staked and monumented, are found in the accepted Notice/Certificate of Location as part of the official case file, managed by the BLM State Office having jurisdiction over the claim.The geometries are created in multiple ways but are primarily derived from Legal Land Descriptions (LLD) for the case and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. Each case is given a data quality score based on how well it mapped. These can be lumped into seven groups to provide a simplified way to understand the scores. Group 1: Direct PLSS Match. Scores “0”, “1”, “2”, “3” should all have a match to the PLSS data. There are slight differences, but the primary expectation is that these match the PLSS. Group 2: Calculated PLSS Match. Scores “4”, “4.1”, “5”, “6”, “7” and “8” were generated through a process of creating the geometry that is not a direct capture from the PLSS. They represent a best guess based on the underlining PLSS Group 3 – Mapped to Section. Score of “8.1”, “8.2”, “8.3”, “9” and “10” are mapped to the Section for various reasons (refer to log information in data quality field). Group 4- Combination of mapped and unmapped areas. Score of 15 represents a case that has some portions that would map and others that do not. Group 5 – No NLSDB Geometry, Only Attributes. Scores “11”, “12”, “20”, “21” and “22” do not have a match to the PLSS and no geometry is in the NLSDB, and only attributes exist in the data. Group 6 – Mapped to County. Scores of “25” map to the County. Group 7 – Improved Geometry. Scores of “100” are cases that have had their geometry edited by BLM staff using ArcGIS Pro or MLRS bulk upload tool.

During the week ending December 31, 2022, about 204,000 initial unemployment claims were made. This is a decrease from the week prior, when initial unemployment claims stood at 223,000. The number of unemployment claims tends to fluctuate rapidly in response to national or global events such as shortages, pandemics, and wars. Initial unemployment claims reached a record high during the COVID-19 pandemic, reaching nearly seven million unique initial claims by the end of March, 2020. The restaurant and retail industries in the United States were particularly impacted.