This data set is used for actuarial and financial application regression modeling case studiesThese data are provided by the Swedish non life insurance commission and include the data of auto insurance claims in 2010.The result of interest is the number (frequency) of claims and the total amount of payments (severity), in SEK. Results based on the driving distance of 5 types of vehicles, it is subdivided according to 7 geographical regions, 7 types of recent driver claim experience and 9 types of vehicles.

Comprehensive dataset containing 56 verified American National Insurance Company - District Office locations in United States with complete contact information, ratings, reviews, and location data.

Description This dataset and project are part of ClaimWise AI, an intelligent automation service designed to streamline auto insurance claim processing. All data in this release was collected and curated by our team, ensuring originality and alignment with real-world claim processing scenarios.

What’s inside

• claim_metadata.xlsx: Structured metadata of insurance claims including claim IDs, incident details, vehicle attributes, and fraud indicators.
• Auto-claim service pipeline: Demonstrates how to leverage machine learning, embeddings, and multimodal AI to process claim data and simulate real-world automation.

Note on Images The pipeline references car crash and accident images as part of embedding and similarity checks. These images were also collected by our team from publicly available resources and curated for research purposes. They are not redistributed in this dataset but are used internally to illustrate how ClaimWise AI can handle multimodal data.

Key Features

• Automates claim intake and metadata standardization.
• Uses AI embeddings for fraud detection and duplicate claim checks.
• Supports integration with curated external crash images for damage assessment.
• Provides an end-to-end demo of an AI-powered insurance claim automation workflow.

Use Cases

• Fraud detection in auto claims
• Accident severity classification
• Claim similarity and duplicate detection
• AI-enabled insurance process optimization

United States Property & Casualty Insurance: Combined Ratio: Other Liability - Claims-Made data was reported at 93.700 % in 2021. This records a decrease from the previous number of 100.400 % for 2020. United States Property & Casualty Insurance: Combined Ratio: Other Liability - Claims-Made data is updated yearly, averaging 97.600 % from Dec 2009 (Median) to 2021, with 13 observations. The data reached an all-time high of 103.400 % in 2016 and a record low of 88.100 % in 2014. United States Property & Casualty Insurance: Combined Ratio: Other Liability - Claims-Made data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG013: Property & Casualty Insurance: Combined Ratio by Lines of Business.

United States Health Insurance: Claims Per Member Per Month: Medicare data was reported at 1,111.000 USD in 2023. This records an increase from the previous number of 1,012.000 USD for 2022. United States Health Insurance: Claims Per Member Per Month: Medicare data is updated yearly, averaging 791.000 USD from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 1,111.000 USD in 2023 and a record low of 746.230 USD in 2007. United States Health Insurance: Claims Per Member Per Month: Medicare data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

Bulgaria Non Life Insurance Payments: National Insurance Company (DZI) data was reported at 69,770,369.000 BGN in Jun 2024. This records an increase from the previous number of 25,803,916.000 BGN for Mar 2024. Bulgaria Non Life Insurance Payments: National Insurance Company (DZI) data is updated monthly, averaging 50,639,002.475 BGN from May 2007 (Median) to Jun 2024, with 194 observations. The data reached an all-time high of 128,571,149.000 BGN in Dec 2023 and a record low of 4,852,487.550 BGN in Jan 2014. Bulgaria Non Life Insurance Payments: National Insurance Company (DZI) data remains active status in CEIC and is reported by Financial Supervision Commission. The data is categorized under Global Database’s Bulgaria – Table BG.Z008: Non Life Insurance Payments: by Company.

BackgroundThough insurance claims data are useful for researching asthma, they have important limitations, such as a diagnostic inaccuracy and a lack of clinical information. To overcome these drawbacks, we used the novel method by merging the clinical data from our asthma cohort with the National Health Insurance (NHI) claims data.Methods and ResultsLongitudinal analysis of asthma-related healthcare use from the NHI claims database, merged with data of 736 patients registered in a Korean asthma cohort, was conducted for three consecutive years from registration of the cohort. Asthma-related asthma healthcare referred to outpatient and emergency department visits, hospitalizations, and the use of systemic corticosteroids. Univariate and multivariate logistic regression analysis was used to evaluate risk factors for asthma-related healthcare. Over three years after enrollment, many patients changed from tertiary to primary/secondary hospitals with a lack of maintenance of inhaled corticosteroid-based controllers. An independent risk factor for emergency visits was a previous history of asthma exacerbation. In hospitalizations, old age and Asthma Control Test (ACT) score variability were independent risk factors. An independent risk factor for per person cumulative duration of systemic corticosteroids was the FEV1 (Forced expiratory volume in one second)%. The use of systemic corticosteroids was independently associated with being female, the FEV1%, and ACT score variability.ConclusionWe found that old age, being female, long-standing asthma, a low FEV1%, asthma brittleness, asthma drug compliance, and a history of asthma exacerbation were independent risk factors for increased asthma-related healthcare use in Korea.

Bulgaria Non Life Insurance Premium Income: National Insurance Company (DZI) data was reported at 196,637,051.000 BGN in Jun 2024. This records an increase from the previous number of 93,332,397.000 BGN for Mar 2024. Bulgaria Non Life Insurance Premium Income: National Insurance Company (DZI) data is updated monthly, averaging 103,116,739.175 BGN from May 2007 (Median) to Jun 2024, with 194 observations. The data reached an all-time high of 354,330,493.000 BGN in Dec 2023 and a record low of 12,317,166.960 BGN in Jan 2011. Bulgaria Non Life Insurance Premium Income: National Insurance Company (DZI) data remains active status in CEIC and is reported by Financial Supervision Commission. The data is categorized under Global Database’s Bulgaria – Table BG.Z007: Non Life Insurance Premium Income: by Company.

Comprehensive dataset containing 22 verified National Insurance locations in United States with complete contact information, ratings, reviews, and location data.

Overview of Data Sources

Flooding Event Data: The flooding event summaries were developed using the NOAA Storm Events Database, available for download at NOAA National Centers for Environmental Information website. While there are many weather events provided in the NOAA Storm Events Database, only the following values were selected for inclusion in the locality summaries: coastal flood, flash flood, flood, heavy rain, hurricane (typhoon), and tropical storm. Detailed descriptions of event types are provided in Appendix A of NOAA's National Weather Service documentation. The data included in this summary includes events recorded from January 1996 through August 2021.

FEMA National Flood Insurance Program Claims: The NFIP claims data were obtained through the FIMA NFIP Redacted Claims data, available through the OpenFEMA data portal. The data used in this analysis was last updated December 6, 2021.

While every effort has been made to obtain current information about the flood events and flood insurance claims contained herein, no representation or assurance is made regarding the accuracy of the underlying data. Please contact HRDPC staff with questions regarding this dashboard product.

Context

The company has shared its annual car insurance data. Now, you have to find out the real customer behaviors over the data.

Content

The columns are resembling practical world features. The outcome column indicates 1 if a customer has claimed his/her loan else 0. The data has 19 features from there 18 of them are corresponding logs which were taken by the company.

Acknowledgements

Mostly the data is real and some part of it is also generated by me.

Inspiration

The data is so well balanced that it will help kagglers find a better intuition of real customers and find the deepest story lien within it.

The Outsourced Insurance Investigative Services market is booming, reaching $513.4 million in 2025 and projected to grow at a 7.4% CAGR through 2033. Learn about market drivers, trends, and key players in this comprehensive analysis. Discover regional market share data and insights into future growth.

Comprehensive dataset containing 40 verified American National Insurance locations in United States with complete contact information, ratings, reviews, and location data.

This dataset provides information on structures that have had multiple National Flood Insurance (NFIP) claims across the history of the program. The data contains NFIP-insured structures that fall within the four categories of Repetitive Loss and Severe Repetitive Loss that FEMA tracks. Definitions of these categories are provided in the field descriptions. There are also fields to show whether a structure is currently NFIP-insured, has been mitigated, and other characteristics. The data includes properties that have since been mitigated or demolished and may no longer considered to be in any of the listed categories.rnLocation information has been redacted to protect personal privacy. Location information is derived from reported address, geocoding of that address, and reported NFIP community. Because NFIP insurance claims data spans the history of the NFIP, many of the structures have poor address information resulting in poor or missing coordinates and additional location fields that rely on those coordinates. An effort has been made to fill in missing data and resolve conflicts between state, county, community, and census block group. Because of this effort, emstatistics derived from this data may differ from those reported elsewhere by FEMA or others/em.rnThere is a lot of interest in this data as it touches many program areas of the NFIP and serves as an indicator of flood risk and mitigation need.rnrnFEMA's terms and conditions and citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.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.

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.

United States Health Insurance: Claims Per Member Per Month: Medicaid data was reported at 398.000 USD in 2023. This records an increase from the previous number of 375.000 USD for 2022. United States Health Insurance: Claims Per Member Per Month: Medicaid data is updated yearly, averaging 291.000 USD from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 398.000 USD in 2023 and a record low of 182.340 USD in 2007. United States Health Insurance: Claims Per Member Per Month: Medicaid data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

1Adjusted for age, sex, and variables that had a p-value

The data set can be crucial in predicting insurance claims in the US or fraud cases in insurance firms

The Data set is National Health Insurance Scheme (NHIS) data with excess zero count. The data were obtained from health Ogun State health facility, Ota, Ogun State. Claims made by 116 users of National Health Insurance Scheme (NHIS) user's from September 2016 to July 2017. Response variable is Number of Encounter, while predictors are Sex, Age of patients, number of drugs prescribed (DrugAdm), and Number of drugs out of stock (DrugOS). The data is over-dispersed with dispersion parameter of 1.4980 since the dispersion parameter is greater than 1. Model such as Bayesian and frequentist techniques can be used to model the data. Such work can be found in the study by Adesina et. al (2017), Adesina et. al (2018), Adesina et al (2019), Dare et al (2019), Adesina et. al (2021).

Claims Analytics for Auto Insurance Market Outlook

As per our latest research, the global market size for Claims Analytics for Auto Insurance stood at USD 2.85 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.2% projected through the forecast period. By 2033, the market is expected to reach approximately USD 8.37 billion, driven by the increasing adoption of advanced analytics, artificial intelligence, and machine learning technologies in the auto insurance sector. This remarkable growth is primarily fueled by the industry's urgent need to enhance claims processing efficiency, reduce fraudulent activities, and improve customer satisfaction through data-driven insights.

The growth trajectory of the Claims Analytics for Auto Insurance market is largely attributed to the surging volume of auto insurance claims and the rising complexity of claim types. Insurers are increasingly challenged by the need to process vast amounts of unstructured and structured data, which has made traditional methods inefficient and prone to errors. The integration of advanced analytics solutions enables insurers to automate claim validation, streamline processing, and identify anomalies that may indicate fraud. Furthermore, the ongoing digital transformation in the insurance industry, coupled with the proliferation of connected vehicles and telematics, is generating unprecedented data volumes, further necessitating the adoption of robust claims analytics platforms.

Another significant growth driver is the heightened focus on fraud detection and risk assessment. The auto insurance industry faces substantial losses due to fraudulent claims, which not only impact profitability but also erode customer trust. Claims analytics platforms leverage machine learning algorithms, predictive modeling, and big data analytics to detect suspicious patterns and flag potentially fraudulent activities in real time. This proactive approach not only minimizes financial losses but also streamlines the investigation process, allowing insurers to allocate resources more efficiently. As regulatory scrutiny intensifies and compliance requirements become more stringent, the demand for transparent, data-driven claims management processes is expected to surge, further propelling market expansion.

The evolution of customer expectations is also shaping the claims analytics landscape. Modern policyholders demand faster, more transparent, and personalized services, especially during the claims process, which is a critical touchpoint in the customer journey. Claims analytics empowers insurers to deliver tailored experiences by leveraging insights from customer data, historical claims, and behavioral patterns. This enables insurers to provide proactive communication, expedite settlements, and offer customized products, thereby enhancing customer loyalty and retention. The integration of omnichannel communication platforms and self-service portals, supported by analytics, is further transforming the way insurers interact with their clients, making claims management more agile and customer-centric.

Regionally, North America continues to dominate the Claims Analytics for Auto Insurance market, supported by a mature insurance ecosystem, high digital adoption rates, and significant investments in advanced technologies. Europe follows closely, driven by regulatory mandates and the growing adoption of telematics-based insurance products. The Asia Pacific region is emerging as a lucrative market, fueled by rapid urbanization, increasing vehicle ownership, and the digitalization of insurance processes in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as insurers in these regions recognize the value of analytics in optimizing claims operations and improving profitability.

Component Analysis

The Component segment of the Claims Analytics for Auto Insurance market is bi