Vehicle Insurance Fraud Detection

Vehicle insurance fraud involves conspiring to make false or exaggerated claims involving property damage or personal injuries following an accident. Some common examples include staged accidents where fraudsters deliberately “arrange” for accidents to occur; the use of phantom passengers where people who were not even at the scene of the accident claim to have suffered grievous injury, and make false personal injury claims where personal injuries are grossly exaggerated.

About this dataset

This dataset contains vehicle dataset - attribute, model, accident details, etc along with policy details - policy type, tenure etc. The target is to detect if a claim application is fraudulent or not - FraudFound_P

Across Australia, the car insurance landscape is entering a new era of digital competition and data-driven risk management. Recent results show premium growth under pressure from higher claims costs, even as demand holds steady, with online platforms pulling consumer attention towards faster, more transparent service. Telematics-based pricing and app-driven claims are becoming the norm, reshaping the customer experience and forcing traditional players to lift their tech game. The car insurance market has also faced more frequent natural disasters and tighter regulatory scrutiny, pushing insurers to bolster capital resilience and risk analytics. A clear signal of the shift came in late 2024, when Suncorp announced a $560.0 million digital upgrade to embed AI and power its next chapter of expansion. Rising costs and expanding exposure have defined the market’s performance. Comprehensive premiums rose about 42% since 2019, to an average of roughly $1,052 in 2024, while claims costs climbed about 42% from mid-2019 to mid-2024. Higher repair prices, more expensive parts and labour and surging vehicle values fed a tighter premium cycle and a growing number of registered vehicles widened the insured base. The rise of online aggregators and digital competitors intensified price pressure, squeezing margins and pushing firms to differentiate with tailored coverage and quicker, more transparent claims handling. Nonetheless, the industry benefited from a larger pool of customers and the accelerating use of data to price risk more accurately. Overall, industry revenue is expected to climb at an annualised 2.7% over the five years through 2025-26 to reach $32.7 billion, including an upswing of 0.8% in the current year. Looking ahead, digital disruptions and climate risks are set to shape the industry’s trajectory. Telematics, AI underwriting and insurtech entrants will keep driving efficiency and personalised pricing, while regulators push for stronger climate risk disclosures and resilience planning. Product innovation – usage-based plans, EV-focused coverage and tailored bundles – will help insurers attract and retain customers in a crowded market. Premiums may stabilise as inflation eases, but claims costs tied to extreme weather will keep pressure on pricing. With competition unlikely to abate, firms will pursue scale, partnerships and data-driven cross-selling to defend market share and some consolidation is likely as players invest in digital capabilities to stay competitive. Overall, industry revenue is forecast to expand at an annualised 1.6% through the end of 2030-31 to total $35.3 billion.

This layer shows the percentage of the civilian noninstitutionalized population who do not have insurance. This is shown by census tract centroids. The data values are from the 2012-2016 American Community Survey 5-year estimate in the B27001 Table for health insurance coverage status broken down by by age and sex characteristics.This map helps to answer a few questions:How many people in the United States don't have health insurance?Where are the concentrations of uninsured population?This map helps to tell a local pattern about insurance in the United States. The data can be stratified by different age and sex characteristics in order to create additional maps. By default, the pop-up provides a breakdown of total male and female uninsured population. This data was downloaded from the United States Census Bureau American Fact Finder on March 1, 2018. It was then joined with 2016 vintage centroid points and hosted to ArcGIS Online and into the Living Atlas. The data contains additional attributes that can be used for mapping and analysis. Nationally, the breakdown of insurance for the civilian noninstitutionalized population in the US is:

Total: 313,576,137 +/-10,365

Male: 153,162,940 +/-12,077

Under 6 years: 12,227,441 +/-11,224

With health insurance coverage 11,643,526 +/-12,783

No health insurance coverage 583,915 +/-6,438

6 to 17 years: 25,282,489 +/-12,396

With health insurance coverage 23,659,835 +/-16,339

No health insurance coverage 1,622,654 +/-14,500

18 to 24 years: 15,350,990 +/-8,369

With health insurance coverage 12,112,729 +/-19,586

No health insurance coverage 3,238,261 +/-24,081

25 to 34 years: 20,901,264 +/-8,155

With health insurance coverage 15,669,472 +/-36,401

No health insurance coverage 5,231,792 +/-38,887

35 to 44 years: 19,499,072 +/-6,321

With health insurance coverage 15,722,620 +/-41,969

No health insurance coverage 3,776,452 +/-41,916

45 to 54 years: 20,965,500 +/-5,283

With health insurance coverage 17,819,431 +/-33,014

No health insurance coverage 3,146,069 +/-31,181

55 to 64 years: 19,068,251 +/-3,959

With health insurance coverage 17,076,497 +/-20,830

No health insurance coverage 1,991,754 +/-19,813

65 to 74 years: 12,168,198 +/-3,453

With health insurance coverage 12,041,594 +/-4,736

No health insurance coverage 126,604 +/-3,207

75 years and over: 7,699,735 +/-3,458

With health insurance coverage 7,657,815 +/-3,794

No health insurance coverage 41,920 +/-1,719

Female: 160,413,197 +/-8,724

Under 6 years: 11,684,980 +/-10,395

With health insurance coverage 11,115,775 +/-13,062

No health insurance coverage 569,205 +/-7,132

6 to 17 years: 24,280,468 +/-11,445

With health insurance coverage 22,723,174 +/-14,642

No health insurance coverage 1,557,294 +/-13,468

18 to 24 years: 15,151,707 +/-5,432

With health insurance coverage 12,591,379 +/-16,744

No health insurance coverage 2,560,328 +/-18,826

25 to 34 years: 21,367,510 +/-4,829

With health insurance coverage 17,505,087 +/-32,122

No health insurance coverage 3,862,423 +/-31,651

35 to 44 years: 20,279,901 +/-4,751

With health insurance coverage 17,146,763 +/-32,076

No health insurance coverage 3,133,138 +/-31,659

45 to 54 years: 21,975,842 +/-5,087

With health insurance coverage 19,083,932 +/-27,415

No health insurance coverage 2,891,910 +/-25,022

55 to 64 years: 20,665,987 +/-3,867

With health insurance coverage 18,537,874 +/-18,484

No health insurance coverage 2,128,113 +/-16,614

65 to 74 years: 13,896,484 +/-3,882

With health insurance coverage 13,730,727 +/-6,177

No health insurance coverage 165,757 +/-3,857

75 years and over: 11,110,318 +/-3,977

With health insurance coverage 11,037,661 +/-4,391

No health insurance coverage 72,657 +/-2,120 Data note from the US Census Bureau:[ACS] data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Factori houses an extensive dataset of US People data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US People Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our People data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US People Data Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

The National Association of County and City Health Officials' (NACCHO) Forces of Change Survey is an evolution of NACCHO's Job Losses and Program Cuts Surveys (also known as the Economic Surveillance Surveys) which measured the impact of the economic recession on local health departments' (LHD) budgets, staff, and programs. The Forces of Change Survey continues to measure changes in LHD budgets, staff, and programs and assess more broadly the impact of forces affecting change in LHDs, such as health reform and accreditation. More specifically, the survey collected information about LHD staffing levels, workforce reductions, and changes in budget sizes; provided services or functions; changes in the level of service delivery; billing for clinical services; efforts to help people enroll in health insurance from exchanges under the Affordable Care Act; awareness of and involvement in the State Innovation Models Initiative; participation in the Public Health Accreditation Board's national accreditation program for LHDs; and whether LHDs are part of a combined health and human services agency. The collection is comprised of the public-use version (Restricted-Use Level 1) of the Forces of Change 2014 dataset, and includes 133 variables for 648 cases, with demographic variables related to LHD budgets, governance type, and number of employees.

This layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black)This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Texas Department of Insurance (TDI) is responsible for licensing, registering, certifying, and regulating agencies and businesses that want to sell insurance or adjust property and casualty claims in Texas. This data set includes a row for each license held by an agency or business. An agency or business with more than one license will be listed in multiple rows. To view a list of people licensed by TDI, go to the Insurance agent and adjusters data set. To learn more about the type of licenses in this data set, go to TDI’s agent and adjuster licensing webpage. For detailed search results on individual agencies, agents, and adjusters please click here: Detailed reports.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

A dataset of COVID-19 testing sites. A dataset of COVID-19 testing sites. If looking for a test, please use the Testing Sites locator app. You will be asked for identification and will also be asked for health insurance information. Identification will be required to receive a test. If you don’t have health insurance, you may still be able to receive a test by paying out-of-pocket. Some sites may also: - Limit testing to people who meet certain criteria. - Require an appointment. - Require a referral from your doctor. Check a location’s specific details on the map. Then, call or visit the provider’s website before going for a test.

Discover the booming Brazilian car insurance market! Projected to reach [insert projected 2033 value from chart data] million by 2033 with a 5.49% CAGR, this comprehensive analysis explores market drivers, trends, and top players like Bradesco and Amil. Learn about segmentations, distribution channels, and future growth potential. Recent developments include: June 2023: Brazil is all set to partially introduce a federal diesel tax this year to bring down automobile costs for the people at large. Tax credits would be offered as incentives to automobile manufacturers who opt to bring down the prices of their respective models., April 2023: Justos, a Brazil-based auto InsurTech startup, raised USD 5.5 million in funding. Justus is different which offers auto insurance with more driver-friendly pricing. Justus uses machine learning to create models that can predict claims and, as a result, charges an individualized value for each driver.. Key drivers for this market are: The adoption of Digital Channels for Purchasing and Managing Insurance Policies, Increasing Awareness of the Importance of Car Insurance for Financial Protection. Potential restraints include: The adoption of Digital Channels for Purchasing and Managing Insurance Policies, Increasing Awareness of the Importance of Car Insurance for Financial Protection. Notable trends are: Increasing Registrations of Electric Vehicles in Brazil.

According to Cognitive Market Research, the global Insurance Advertising market size was USD 11542.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 12.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 4617.04 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a USD 3462.78 million market size.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 2654.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1352.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 230.85 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
The Life Insurance held the highest Insurance Advertising market revenue share in 2024.

Market Dynamics of

Insurance Advertising Market

Key Drivers for Insurance Advertising Market

Growing demand for personalized insurance products drives targeted advertising to increase the demand globally

The Insurance Advertising market is experiencing growth owing to the growing demand for personalized insurance products that drive targeted advertising. As consumers look for insurance policies tailored to their specific needs, companies increasingly use targeted advertising for these people. This approach helps insurers better engage with potential customers, leading to greater engagement and better results. The focus on personalization is leading companies to invest in advanced advertising techniques such as data analytics and AI, to better understand consumer preferences. While targeted advertising is essential in attracting consumers, the insurance advertising market sees tremendous growth, with companies competing to capture the attention of their audience.

Increasing digitalization boosts online insurance advertising channels to propel market growth

The Insurance Advertising market has witnessed steady growth, driven by Increasing digitalization boosts online insurance advertising channels. As more people use digital channels to research and buy insurance, companies are shifting their online advertising efforts to reach more people. This move towards digital channels allows insurers to better target potential customers, tailoring their ads with data-driven strategies. The simplicity and reach of online advertising drive higher engagement and conversion rates. As a result, the insurance advertising market continues to grow, with companies focusing on digital channels to attract and retain customers.

Key Restraint for Insurance Advertising Market

High Cost of Advanced Phototherapy Devices Limit The Sales of the Market

The Insurance Advertising market is restrained by the high cost of advanced phototherapy devices. For small insurers, the cost of running large-scale advertising campaigns can be prohibitive, making it difficult to compete with larger companies with larger budgets. These budget constraints often prevent them from reaching people or do not advertise their products well. Consequently, market growth slows because only large players can fully exploit advertising opportunities. The challenge for small businesses is to find cost-effective ways to advertise, which can restrict both their market presence and influence in the industry.

Key Trend for Insurance Advertising Market

The insurance business is transformed by digital-first engagement and hyper-personalization.

The insurance advertising market is going through a data-driven revolution, utilizing AI-powered consumer insights to provide hyper-personalized campaigns that are tailored to each individual's life stage, risk profile, and real-time behavior. With dynamic creative optimization (DCO), ads can automatically change their messaging, graphics, and offerings depending on user data, such as weather-related risks (floods, storms) or life events (marriage, home purchase).

The focus is on video-first storytelling, with brief instructional ...

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses. Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables. Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021. This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data. This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score. This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4. The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting. These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons. For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

According to Cognitive Market Research, the global Professional Liability Insurance market size is USD 42815.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 3.90% from 2024 to 2031.

North America holds the major market of more than 40% of the global revenue with a market size of USD 17126.08 million in 2024 and will develop at a compound annual growth rate (CAGR) of 2.1% from 2024 to 2031.
Europe accounts for a share of over 30% of the global market size of USD 12844.56 million.
Asia Pacific holds the market of around 23% of the global revenue with a market size of USD 9847.50 million in 2024 and will develop at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
Latin America holds the market of more than 5% of the global revenue with a market size of USD 2140.76 million in 2024 and will develop at a compound annual growth rate (CAGR) of 3.3% from 2024 to 2031.
Middle East and Africa holds the major market of around 2% of the global revenue with a market size of USD 856.30 million in 2024 and will develop at a compound annual growth rate (CAGR) of 3.6% from 2024 to 2031.
SMEs are the predominant category. A lot of small businesses are optimistic about the future because they plan to invest in their businesses and expect their income to expand.

Market Dynamics of Professional Liability Insurance Market

Key Drivers for Professional Liability Insurance Market

Urbanization and Transformation towards Service-sector Economy to Expedite Market Growth:

The economy is always evolving to meet the expanding demands of consumers. Over the past ten years, there has been a growth in service-oriented businesses, which could yield greater profits than the manufacturing industry. The service industries offer services as a fix for current issues. The development of the internet has made information and data easily accessible, which has led to the emergence of service-based enterprises. Furthermore, the service's structure and quality were enhanced by the use of advanced technology, making it easier to access, more affordable, more effective, and less time-consuming. Larger companies are attracting investment from developing nations due to their global standards, commitment to quality, and capacity to train talented workers, all of which have a long-term impact on the nation's economy.

Increase in Awareness of the "Professional Liability Insurance Plan to Boost the Market Demand:

Professionals are not just found in the technology industry. Doctors, architects, lawyers, and other specialized specialists are becoming more and more prevalent. The internet has allowed for universal access to free education. Customers are able to independently investigate the benefits of the insurance plan. In addition, corporations and professionals have become interested in the government policies of the past 10 years and the widespread convergence of media. Programmers providing financial education are being encouraged by stakeholders and organizations. The campaigns by banks and the government were crucial in raising awareness. In addition, the growing number of firms, rising customer expectations, and population growth have forced them to choose professional liability insurance in order to reduce risk.

Restraint Factor for the Professional Liability Insurance Market

High Insurance Amount and Longer Time for Claim Settlement to Act as a Restraining Factor:

The necessity of health insurance in unpredictable times has been highlighted by the pandemic. But with inflation and the rising cost of healthcare, it is just too expensive for the typical person to afford. The insurance providers ought to lower the cost for middle-class consumers. Aside from this, there have been situations when the money for a claim was denied or where the claim took a lengthy time to resolve. People avoid insurance because they perceive it to be a bad decision and because these experiences have combined in their thoughts. The market for professional liability insurance will be constrained by ignorance, false beliefs, and instances in the past.

Complexity in Policy Customization Across Professions:

Professional liability insurance must be customized for distinct sectors including healthcare, legal services, consulting, and IT. The significant differences in professional risks complicate the standardization of products for insurers, thereby elevati...

This survey is a component of the Robert Wood Johnson Foundation's Health Tracking Initiative, a program designed to monitor changes within the health care system and their effects on people. Focusing on care and treatment for alcohol, drug, and mental health conditions, the survey reinterviewed respondents to the 1996-1997 CTS Household Survey (COMMUNITY TRACKING STUDY HOUSEHOLD SURVEY, 1996-1997, AND FOLLOWBACK SURVEY, 1997-1998: [UNITED STATES] [ICPSR 2524]). Topics covered by the questionnaire include (1) demographics, (2) health and daily activities, (3) mental health, (4) alcohol and illicit drug use, (5) use of medications, (6) health insurance coverage including coverage for mental health, (7) access, utilization, and quality of behavioral health care, (8) work, income, and wealth, and (9) life difficulties. Five imputed versions of the data are included in the collection for analysis with multiple imputation techniques.

Project Objectives Provider Fraud is one of the biggest problems facing Medicare. According to the government, the total Medicare spending increased exponentially due to frauds in Medicare claims. Healthcare fraud is an organized crime which involves peers of providers, physicians, beneficiaries acting together to make fraud claims.

Rigorous analysis of Medicare data has yielded many physicians who indulge in fraud. They adopt ways in which an ambiguous diagnosis code is used to adopt costliest procedures and drugs. Insurance companies are the most vulnerable institutions impacted due to these bad practices. Due to this reason, insurance companies increased their insurance premiums and as result healthcare is becoming costly matter day by day.

Healthcare fraud and abuse take many forms. Some of the most common types of frauds by providers are:

a) Billing for services that were not provided.

b) Duplicate submission of a claim for the same service.

c) Misrepresenting the service provided.

d) Charging for a more complex or expensive service than was actually provided.

e) Billing for a covered service when the service actually provided was not covered.

Problem Statement The goal of this project is to " predict the potentially fraudulent providers " based on the claims filed by them.along with this, we will also discover important variables helpful in detecting the behaviour of potentially fraud providers. further, we will study fraudulent patterns in the provider's claims to understand the future behaviour of providers.

Introduction to the Dataset For the purpose of this project, we are considering Inpatient claims, Outpatient claims and Beneficiary details of each provider. Lets s see their details :

A) Inpatient Data

This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code.

B) Outpatient Data

This data provides details about the claims filed for those patients who visit hospitals and not admitted in it.

C) Beneficiary Details Data

This data contains beneficiary KYC details like health conditions,regioregion they belong to etc.