Insurance agent email list is a list of email addresses of insurance agents. It helps you reach many agents fast. You can use it for business, offers, or updates. It includes names, email addresses, phone numbers, company names, and locations. That also shows what type of insurance they sell—life, health, auto, or home. Many companies use this list. Insurance firms use it to share new plans. Software companies use it to sell tools for agents. Training centers use it to invite agents to courses or events. You can buy an insurance agent email list for direct marketing. These numbers are more reliable and updated often and have fewer errors.

Insurance agent email list is a helpful and crucial tool for direct marketing. We follow email and privacy laws. Agents must agree to get emails. This is called “opt-in.” If not, your email may go to spam or cause legal problems. You can build your own list or get one from a trusted company. Some lists are national. Others focus on local areas. A valid email directory saves time and helps grow your business. You can send news, offers, or job info with one click. It builds trust and strong connections. In short, an insurance agent email list is a smart tool. It helps with marketing, sales, and hiring. When used right, it brings incredible results. Insurance agent email database will help any business to gain a huge return on investment (ROI). Besides, you can communicate with many well-known company owners by sending text messages to them. Furthermore, they will get to know about the brand and products directly. Also, we follow the GDPR rules and rules completely. It will make your business more profitable through digital campaigns. If any business person wants to get more earnings, they have to buy contacts from List to Data. Likewise, we assure you that our service cost is more affordable. This will help to do online marketing in B2B and B2C platforms. So, if you face any issue in taking our service, we have a proper solution for this.

Vietnam Insurance Companies: Total Labour: Employees & Agents data was reported at 786,795.000 Person in 2017. This records an increase from the previous number of 603,089.000 Person for 2016. Vietnam Insurance Companies: Total Labour: Employees & Agents data is updated yearly, averaging 143,540.000 Person from Dec 1993 (Median) to 2017, with 19 observations. The data reached an all-time high of 786,795.000 Person in 2017 and a record low of 1,000.000 Person in 1993. Vietnam Insurance Companies: Total Labour: Employees & Agents data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Vietnam – Table VN.Z003: Insurance Statistics.

Access verified data on 1 million banking and insurance companies worldwide. Sourced from official trade registers and part of our global 380M+ company database. Ideal for compliance, sales, and analytics—delivered via API, bulk files, or platform.

The Texas Department of Insurance (TDI) regulates the state’s insurance industry and oversees the administration of the Texas workers’ compensation system. This data set includes counts for confirmed complaints and policies in force by company name and line of coverage. The complaint index is calculated by dividing the company's percentage of complaints for a specific line of insurance by the company's percentage of policies in force for the same line of insurance. The average index is 1.00. A number less than 1 indicates fewer complaints than average. A number greater than 1 indicates more complaints than average. Please visit TDI Complaints: All Data for details about processed complaints.

China Insurance Premium: LI: Professional Insurance Agency data was reported at 91,897.000 RMB mn in 2022. This records an increase from the previous number of 66,015.620 RMB mn for 2021. China Insurance Premium: LI: Professional Insurance Agency data is updated yearly, averaging 7,579.000 RMB mn from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 91,897.000 RMB mn in 2022 and a record low of 973.000 RMB mn in 2005. China Insurance Premium: LI: Professional Insurance Agency data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGD: Insurance Premium.

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. Public Use Tape 16 is the second public use data release from the NMES Health Insurance Plans Survey (HIPS). The purpose of the HIPS was to verify information reported by respondents to two components of the NMES, the Household Survey and the Survey of American Indians and Alaska Natives (SAIAN), about their health insurance coverage. Additional details were also obtained from the employers, unions, and insurance companies through which coverage was provided. Parts 1 and 2 of Public Use Tape 16 are files that can be used to link data to Household Survey policyholders in NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: POLICYHOLDERS OF PRIVATE INSURANCE: PREMIUMS, PAYMENT SOURCES, AND TYPES AND SOURCE OF COVERAGE PUBLIC USE TAPE 15. These link files permit identification of the records in the Private Health Insurance Benefit Database (Parts 3-17 of this collection) that describe the specific benefits held by the policyholders. These files also permit linkage to the personal and socioeconomic characteristics for these policyholders found in NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: HOUSEHOLD SURVEY, POPULATION CHARACTERISTICS AND PERSON-LEVEL UTILIZATION, ROUNDS 1-4 PUBLIC USE TAPE 13. Future link files will permit linkage of the Benefit Database to persons in the SAIAN and to dependents of policyholders in the Household Survey. The section files of the Benefit Database, Parts 4-13, contain information on Health Maintenance Organizations (HMOs), copayments, basic coverage, hospital and medical services, cost-containment provisions, major medical coverage, dental care, prescription drugs, vision and hearing care, and Medicare benefits. The schedule files, Parts 14-17, contain specific deductible amounts, dollar benefits, coinsurance provisions, maximum benefits, and benefit periods. Wherever possible, copies of policies or booklets describing the coverage and benefits were obtained in order to abstract this information.

The focus of this project was insider fraud -- crimes committed by the owners and operators of insurance companies that were established for the purposes of defrauding businesses and employees. The quantitative data for this collection were taken from a database maintained by the National Association of Insurance Commissioners (NAIC), an organization that represents state insurance departments collectively and acts as a clearinghouse for information obtained from individual departments. Created in 1988, the Regulatory Information Retrieval System (RIRS) database contains information on actions taken by state insurance departments against individuals and firms, including cease and desist orders, license revocations, fines, and penalties imposed. Data available for this project include a total of 123 actions taken against firms labeled as Multiple Employer Welfare Arrangements or Multiple Employer Trusts (MEWA/MET) in the RIRS database. Variables available in this data collection include the date action was taken, state where action was taken, dollar amount of the penalty imposed in the action, and disposition for action taken.

China Insurance Premium: LI: Other Concurrent Business Insurance Agency data was reported at 30,253.390 RMB mn in 2022. This records a decrease from the previous number of 31,251.870 RMB mn for 2021. China Insurance Premium: LI: Other Concurrent Business Insurance Agency data is updated yearly, averaging 16,062.575 RMB mn from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 35,676.340 RMB mn in 2016 and a record low of 1,798.000 RMB mn in 2007. China Insurance Premium: LI: Other Concurrent Business Insurance Agency data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGD: Insurance Premium.

Electronic Health Records for Insurance Market Outlook

According to our latest research, the global Electronic Health Records for Insurance market size reached USD 4.8 billion in 2024, with a robust compound annual growth rate (CAGR) of 10.2% from 2025 to 2033. This sector is expected to continue its upward trajectory, reaching a forecasted value of USD 12.7 billion by 2033. The primary drivers fueling this growth include increasing digitalization of healthcare information, rising demand for efficient claims processing, and the growing necessity for fraud detection and risk assessment in the insurance industry. As per our comprehensive industry analysis, the market's rapid expansion is underpinned by the widespread adoption of advanced EHR solutions tailored specifically for insurance workflows, which are becoming indispensable for insurers, third-party administrators, and government agencies worldwide.

One of the key growth factors for the Electronic Health Records for Insurance market is the escalating demand for automation and streamlining of insurance operations. With healthcare data becoming more complex and voluminous, insurers are under pressure to reduce administrative costs and improve accuracy in claims management. Electronic Health Records (EHR) systems provide a unified and structured digital database, enabling insurance companies to access patient data swiftly and securely. This not only accelerates the claims adjudication process but also minimizes errors and redundancies. Furthermore, the integration of EHRs with insurance platforms enhances transparency and accountability, allowing for real-time decision-making and improved customer satisfaction. The ability to automate routine tasks, such as document verification and eligibility checks, is proving to be a game-changer for insurers aiming to stay competitive in a rapidly evolving market.

Another significant driver propelling the Electronic Health Records for Insurance market is the increasing focus on risk assessment and fraud detection. Insurers are leveraging advanced EHR analytics to identify high-risk cases, detect potential fraud, and make informed underwriting decisions. By integrating artificial intelligence and machine learning algorithms with EHR systems, insurers can analyze vast datasets to uncover patterns indicative of fraudulent activities or adverse health outcomes. This proactive approach not only helps mitigate financial losses but also ensures compliance with regulatory requirements. Additionally, the adoption of EHRs enables insurers to offer personalized products and pricing based on comprehensive health profiles, further enhancing their competitive edge. The convergence of EHR technology with predictive analytics is expected to drive significant innovation and value creation in the insurance sector over the next decade.

The growing emphasis on regulatory compliance and data security is also shaping the trajectory of the Electronic Health Records for Insurance market. With stringent data protection laws such as HIPAA in the United States and GDPR in Europe, insurers are compelled to adopt robust EHR systems that ensure the confidentiality and integrity of sensitive health information. Modern EHR solutions are equipped with advanced encryption, access controls, and audit trails, enabling insurers to safeguard patient data and maintain regulatory compliance. Moreover, the increasing collaboration between healthcare providers and insurers is fostering the development of interoperable EHR platforms, facilitating seamless data exchange and improving the overall efficiency of insurance operations. As regulatory scrutiny intensifies and data breaches become more prevalent, the demand for secure and compliant EHR solutions is expected to surge, further bolstering market growth.

From a regional perspective, North America continues to dominate the Electronic Health Records for Insurance market, accounting for the largest share in 2024 due to its advanced healthcare infrastructure, high adoption of digital health technologies, and supportive regulatory environment. Europe follows closely, driven by government initiatives promoting eHealth and digital transformation in insurance. The Asia Pacific region is emerging as a lucrative market, fueled by rapid urbanization, increasing healthcare expenditure, and the growing penetration of health insurance. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as insurers in these regions gradually embrace digitalization and inve

The S_FIRM_Pan table contains information about the FIRM panel area. A spatial file with location information also corresponds with this data table. The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. Panel boundaries are generally derived from USGS DOQQ boundaries. As a result, the panels are generally rectangular. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction. This information is needed for the FIRM Panel Index and the following tables in the FIS report: Listing of NFIP Jurisdictions, Levees, Incorporated Letters of Map Change, and Coastal Barrier Resources System Information.

The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. Panel boundaries are generally derived from USGS DOQQ boundaries. As a result, the panels are generally rectangular. FIRM panels must not overlap or have gaps within a study. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction.

This information is needed for the FIRM Panel Index and the following tables in the FIS report: Listing of NFIP Jurisdictions, Levees, Incorporated Letters of Map Change, and Coastal Barrier Resources System Information.

This layer is a component of Region Preliminary Data.

The Texas Department of Insurance, Division of Workers' Compensation (DWC) maintains a database of institutional medical billing services (SV2). It contains charges, payments, and treatments billed on a CMS-1450 form (UB-92, UB-04) by hospitals and medical facilities that treat injured employees, excluding ambulatory surgical centers, with dates of service more than five years old. For datasets from the past five years, see institutional medical billing services (SV2) header information. The header identifies insurance carriers, injured employees, employers, place of service, and diagnostic information. The bill header information groups individual line items reported in the detail section. The bill selection date and bill ID must be used to group individual line items into a single bill. Find more information in our institutional medical billing services (SV2) header data dictionary. See institutional medical billing services (SV2) detail information- historical for the corresponding detail records related to this dataset. Go to our page on DWC medical state reporting public use data file (PUDF) to learn more about using this information.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

Context

The market for providing flood insurance policies in the U.S. is almost exclusively backed by the National Flood Insurance Program (NFIP). The NFIP is only available for policies purchased within participating communities, and partners with private insurance companies to distribute flood insurance policies to homeowners and businesses.

Because flooding is the primary vector of economic damages inflicted on local communities as demonstrated by the 2016-2019 hurricane seasons, and given the projected increase in destructive flooding as a result of climate change- there's an enormous need to more efficiently distribute financial risk due to climate change.

Content

This data contains multiple fields about anonymized flood policy holders in the United States:

1. Flood zone rating
2. Building elevation
3. Zip code, county code, census tract, state code, city code
4. Building type
5. Business type
6. Flood insurance deductibles
7. Total insurance coverage
8. Total insurance premium of the policy
9. How the policy was originally rated

Acknowledgements

This data wouldn't be available were it not for the OpenFEMA team- they're the ones primarily responsible for its update and maintenance on its original site: https://www.fema.gov/media-library/assets/documents/180376

(I'm sure they'd appreciate a nice email at OpenFEMA@fema.dhs.gov)

Inspiration

Hurricane season is always right around the corner.

United States QSS: Revenue: FI: Insurance: Agencies, Brokerages & Others data was reported at 95.020 USD bn in Mar 2018. This records an increase from the previous number of 93.801 USD bn for Dec 2017. United States QSS: Revenue: FI: Insurance: Agencies, Brokerages & Others data is updated quarterly, averaging 72.832 USD bn from Sep 2009 (Median) to Mar 2018, with 35 observations. The data reached an all-time high of 95.020 USD bn in Mar 2018 and a record low of 47.961 USD bn in Dec 2009. United States QSS: Revenue: FI: Insurance: Agencies, Brokerages & Others data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H019: Quarterly Services Survey.

Taiwan Insurance Agency: Number of Companies: Life data was reported at 92.000 Unit in 2017. This records a decrease from the previous number of 99.000 Unit for 2016. Taiwan Insurance Agency: Number of Companies: Life data is updated yearly, averaging 120.000 Unit from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 141.000 Unit in 2008 and a record low of 92.000 Unit in 2017. Taiwan Insurance Agency: Number of Companies: Life data remains active status in CEIC and is reported by Taiwan Insurance Institute. The data is categorized under Global Database’s Taiwan – Table TW.Z033: Insurance Statistics: Insurance Agnecy (Annual).

Base Flood Elevations for the 1% annual chance flood.

This layer is a component of Region Preliminary Data.

Taiwan Insurance Agency: Number of Companies data was reported at 291.000 Unit in 2017. This records a decrease from the previous number of 300.000 Unit for 2016. Taiwan Insurance Agency: Number of Companies data is updated yearly, averaging 391.000 Unit from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 514.000 Unit in 2006 and a record low of 291.000 Unit in 2017. Taiwan Insurance Agency: Number of Companies data remains active status in CEIC and is reported by Taiwan Insurance Institute. The data is categorized under Global Database’s Taiwan – Table TW.Z033: Insurance Statistics: Insurance Agnecy (Annual).

Armenia Number of Insurance Companies: with Non Life Insurance License data was reported at 8.000 Unit in Feb 2025. This stayed constant from the previous number of 8.000 Unit for Jan 2025. Armenia Number of Insurance Companies: with Non Life Insurance License data is updated monthly, averaging 7.000 Unit from Dec 2009 (Median) to Feb 2025, with 183 observations. The data reached an all-time high of 12.000 Unit in Aug 2010 and a record low of 7.000 Unit in Jun 2023. Armenia Number of Insurance Companies: with Non Life Insurance License data remains active status in CEIC and is reported by Central Bank of Armenia. The data is categorized under Global Database’s Armenia – Table AM.Z004: Number of Insurance Companies, Brokers and Agreements.

Italy Insurance: Number of Insurance Companies data was reported at 213.000 Unit in 2017. This records a decrease from the previous number of 215.000 Unit for 2016. Italy Insurance: Number of Insurance Companies data is updated yearly, averaging 240.000 Unit from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 249.000 Unit in 2003 and a record low of 213.000 Unit in 2017. Italy Insurance: Number of Insurance Companies data remains active status in CEIC and is reported by National Association of Insurance Companies. The data is categorized under Global Database’s Italy – Table IT.RG003: Insurance: Operational Statistics.