100+ datasets found
  1. Fraudlent claim in healthcare

    • kaggle.com
    Updated Apr 20, 2022
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    Beenu Sharma (2022). Fraudlent claim in healthcare [Dataset]. https://www.kaggle.com/datasets/beenusharma42/fraudlent-claim-in-healthcare
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 20, 2022
    Dataset provided by
    Kaggle
    Authors
    Beenu Sharma
    Description

    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.

  2. Leading reasons for denial of health insurance claims in the U.S. 2016-2020

    • statista.com
    Updated Sep 27, 2022
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    Leading reasons for denial of health insurance claims in the U.S. 2016-2020 [Dataset]. https://www.statista.com/statistics/1332806/denial-reasons-for-health-insurance-claims-in-the-us/
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    Dataset updated
    Sep 27, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Between 2016 and 2020, registration and/or eligibility was the main reason for 26.6 percent of health insurance claims being denied in the United States. Furthermore, missing or invalid claim data caused over 17 percent of health insurance claims to be denied in this time period. This statistic illustrates the leading reasons for denials of healthcare claims in the United States (U.S.) in 2020.

  3. Healthcare Claims Management Market - Size, Report & Share

    • mordorintelligence.com
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    Mordor Intelligence, Healthcare Claims Management Market - Size, Report & Share [Dataset]. https://www.mordorintelligence.com/industry-reports/healthcare-claims-management-market
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    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2019 - 2030
    Area covered
    Global
    Description

    The Healthcare Claims Management Market report segments the industry into Solution Type (Integrated Solutions, Standalone Solutions), Component (Services, Software), Delivery Mode (On-Premise, Cloud-Based), End User (Healthcare Payers, Healthcare Providers, Other End Users), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, South America).

  4. M

    Medical Claims Processing Services Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Apr 30, 2025
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    Archive Market Research (2025). Medical Claims Processing Services Report [Dataset]. https://www.archivemarketresearch.com/reports/medical-claims-processing-services-377039
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    ppt, pdf, docAvailable download formats
    Dataset updated
    Apr 30, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global medical claims processing services market is experiencing robust growth, projected to reach a market size of $25 billion in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 5%. This growth is fueled by several key factors. The increasing prevalence of chronic diseases and the rising demand for advanced medical treatments are driving up the volume of medical claims, necessitating efficient processing solutions. Furthermore, the industry is witnessing a significant shift towards value-based care models, which emphasize the need for accurate and timely claims processing to ensure appropriate reimbursement. Technological advancements, such as the adoption of artificial intelligence (AI) and machine learning (ML) for automated claim adjudication and fraud detection, are enhancing efficiency and reducing processing times. Government regulations mandating electronic claims submission and improved data security are also contributing to market growth. Segmentation within the market reveals strong demand across various applications, including cardiovascular surgery, laparoscopic surgeries, and general surgeries, with claim adjudication and claim repricing segments leading the type-based classification. Major players like Aetna Inc., UnitedHealth Group, and Humana are leveraging these trends to expand their market share, investing in innovative technologies and strategic partnerships. The market's expansion is not without its challenges. Concerns regarding data privacy and security remain paramount, particularly with the increasing reliance on digital platforms. The complex regulatory landscape across different geographies adds to the operational complexities for service providers. However, the ongoing investments in advanced technologies and the increasing adoption of cloud-based solutions are mitigating some of these risks. The geographical distribution of market share reveals strong growth potential in North America and Europe, driven by high healthcare expenditure and technological advancements. Emerging markets in Asia-Pacific are also expected to witness significant growth in the coming years, fueled by rising healthcare awareness and increased government spending on healthcare infrastructure. The forecast period from 2025 to 2033 suggests continued expansion, potentially exceeding $35 billion by 2033.

  5. m

    Healthcare Claims Management Solutions Market CAGR of 7.2%

    • market.us
    csv, pdf
    Updated Jun 9, 2025
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    Market.us (2025). Healthcare Claims Management Solutions Market CAGR of 7.2% [Dataset]. https://market.us/report/healthcare-claims-management-solutions-market/
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    csv, pdfAvailable download formats
    Dataset updated
    Jun 9, 2025
    Dataset provided by
    Market.us
    License

    https://market.us/privacy-policy/https://market.us/privacy-policy/

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Healthcare Claims Management Solutions Market Size is expected to reach US$ 54.9 Bn by 2034, from US$ 27.4 Bn in 2024, at a CAGR of 7.2%.

  6. U

    United States Health Insurance: Claims Adjustment Expenses: Medicare

    • ceicdata.com
    Updated Apr 4, 2021
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    CEICdata.com (2021). United States Health Insurance: Claims Adjustment Expenses: Medicare [Dataset]. https://www.ceicdata.com/en/united-states/health-insurance-operations-by-lines-of-business
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    Dataset updated
    Apr 4, 2021
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2012 - Dec 1, 2023
    Area covered
    United States
    Variables measured
    Insurance Market
    Description

    Health Insurance: Claims Adjustment Expenses: Medicare data was reported at 13.327 USD bn in 2023. This records an increase from the previous number of 10.770 USD bn for 2022. Health Insurance: Claims Adjustment Expenses: Medicare data is updated yearly, averaging 4.766 USD bn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 13.327 USD bn in 2023 and a record low of 1.285 USD bn in 2007. Health Insurance: Claims Adjustment Expenses: 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.

  7. U

    United States Health Insurance: Accident and Health: Net Incurred Claims:...

    • ceicdata.com
    Updated Nov 15, 2021
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    CEICdata.com (2021). United States Health Insurance: Accident and Health: Net Incurred Claims: Stop Loss [Dataset]. https://www.ceicdata.com/en/united-states/health-insurance-accident-and-health-net-incurred-claims-by-lines-of-business
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    Dataset updated
    Nov 15, 2021
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2015 - Dec 1, 2023
    Area covered
    United States
    Variables measured
    Insurance Market
    Description

    Health Insurance: Accident and Health: Net Incurred Claims: Stop Loss data was reported at 22.470 USD bn in 2023. This records an increase from the previous number of 20.941 USD bn for 2022. Health Insurance: Accident and Health: Net Incurred Claims: Stop Loss data is updated yearly, averaging 18.720 USD bn from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 22.470 USD bn in 2023 and a record low of 10.735 USD bn in 2015. Health Insurance: Accident and Health: Net Incurred Claims: Stop Loss 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.RG020: Health Insurance: Accident and Health: Net Incurred Claims by Lines of Business.

  8. I

    Insurance Claims Management Service Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Mar 15, 2025
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    Archive Market Research (2025). Insurance Claims Management Service Report [Dataset]. https://www.archivemarketresearch.com/reports/insurance-claims-management-service-59483
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global Insurance Claims Management market is experiencing robust growth, driven by increasing insurance penetration, rising healthcare costs, and the growing adoption of digital technologies. The market size in 2025 is estimated at $50 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing complexity of insurance claims, particularly in areas like healthcare and business insurance, necessitates specialized management services. Secondly, the demand for improved efficiency and cost reduction in claims processing is driving the adoption of advanced technologies like AI and machine learning within the industry. Thirdly, regulatory changes and a greater focus on customer experience are also pushing insurers to outsource claims management to specialized providers. The market is segmented by insurance type (Business, Medical, Others) and application (BFSI, Government, Travel, Healthcare, Others), offering varied opportunities for different players. The presence of established players like Marsh, WNS, and Deloitte, alongside emerging technology providers, indicates a competitive and dynamic market landscape. The significant growth is anticipated across all regions, with North America and Europe currently dominating the market share due to higher insurance penetration and established infrastructure. However, Asia Pacific is poised for significant expansion driven by rapid economic growth and increasing insurance awareness. While factors like data security concerns and the high initial investment cost of implementing new technologies pose some challenges, the overall market outlook remains positive. The continuous innovation in claims management technologies, coupled with the increasing demand for efficient and transparent processes, will likely sustain the market's growth trajectory throughout the forecast period. The expanding role of Insurtech companies in providing innovative solutions and the growing adoption of cloud-based platforms further contribute to the market's positive outlook.

  9. Dataset for "Public health insurance coverage in India before and after...

    • figshare.com
    bin
    Updated Aug 10, 2023
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    Sanjay K Mohanty; Ashish Kumar Upadhyay; Suraj Maiti; Radhe Shyam Mishra; Fabrice Kämpfen; Jürgen Maurer; Owen O'Donell (2023). Dataset for "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" [Dataset]. http://doi.org/10.6084/m9.figshare.23919078.v1
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    binAvailable download formats
    Dataset updated
    Aug 10, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Sanjay K Mohanty; Ashish Kumar Upadhyay; Suraj Maiti; Radhe Shyam Mishra; Fabrice Kämpfen; Jürgen Maurer; Owen O'Donell
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    India
    Description

    Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey dataThe National Family Health Survey (NFHS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration from the Demographic and Health Survey (DHS) website upon registration at The DHS Program - Request Access To Datasets. We have used data from the fourth and fifth round of NFHS, which can be accessed after registration from the link given here for NFHS 4 and NFHS 5 https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0 and here https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=0 respectively. These datasets (HR file) have been used to obtain this combined dataset of a paper entitled "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" submitted to BMJ Global Health August 2023.

  10. i

    Claims by Zip Code and Category of Services - Dataset - The Indiana Data Hub...

    • hub.mph.in.gov
    Updated Sep 14, 2017
    + more versions
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    (2017). Claims by Zip Code and Category of Services - Dataset - The Indiana Data Hub [Dataset]. https://hub.mph.in.gov/dataset/claims-by-zip-code-and-category-of-services
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    Dataset updated
    Sep 14, 2017
    Area covered
    Indiana
    Description

    This dataset provides information related to the major services for patients. It contains information about the total number of patients, total number of claims, and dollar amount paid, grouped by recipient zip code. Restricted to claims with service date between 01/2012 to 12/2017. Service categories considered are: 01 - Inpatient Service 03 - Outpatient Service 06 - Physician Service 11 - Lab Service 12 - X-Ray Service 17 - Clinic Service 26 - Mental Health Service 27 - Dental Service/Child 28 - Dental Service/Adult 31 - Eye Care and Exams 38 - EPSDT Service Provider is billing provider. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA. Distance between recipient and provider is a straight-line distance calculated and not the physical distance.

  11. d

    Year and Insurer wise Status of Claims of General and Health Insurers by...

    • dataful.in
    Updated Apr 1, 2025
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    Dataful (Factly) (2025). Year and Insurer wise Status of Claims of General and Health Insurers by Number of Policies [Dataset]. https://dataful.in/datasets/21072
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    application/x-parquet, csv, xlsxAvailable download formats
    Dataset updated
    Apr 1, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    All India
    Variables measured
    Status of Claims of General and Health Insurers
    Description

    The dataset contains Year Wise Insurer Wise Status of Claims of General and Health Insurers by Number of Policies from Handbook on Indian Insurance Statistics

    Note: 1. Demerger of general insurance business of Bharti AXA General Insurance Co.Ltd. to ICICI Lombard General Insurance Co.Ltd. w.e.f April 01, 2021. 2. Zuno General Insurance Co. Ltd is formerly known as Edelweiss General Insurance Company Limited

  12. Medical Coding and Billing Services Market Report | Global Forecast From...

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
    + more versions
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    Dataintelo (2025). Medical Coding and Billing Services Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-medical-coding-and-billing-services-market
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    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Medical Coding and Billing Services Market Outlook



    The global market size for medical coding and billing services was valued at approximately USD 15 billion in 2023, and it is projected to reach around USD 30 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5%. This market is experiencing significant growth due to the increasing need for precise and efficient healthcare data management and billing processes. The digitization of healthcare services, coupled with stringent regulatory requirements, has driven the adoption of professional medical coding and billing services across the globe.



    One of the primary growth factors for this market is the rising demand for healthcare services. As the global population ages and the prevalence of chronic diseases increases, more patients require medical attention, leading to a higher volume of medical claims that need to be processed. Efficient medical coding and billing services ensure that healthcare providers receive timely and accurate reimbursements, which is crucial for maintaining their financial health and operational efficiency. Additionally, the transition to value-based care models, which emphasize improved patient outcomes and cost efficiency, further drives the need for accurate medical coding and billing.



    Technological advancements in healthcare IT systems also play a crucial role in the growth of the medical coding and billing services market. The integration of artificial intelligence (AI) and machine learning (ML) in coding and billing processes has significantly enhanced accuracy and reduced the time required for processing claims. These technologies help in minimizing errors, identifying discrepancies, and ensuring compliance with ever-evolving regulatory standards. Furthermore, the widespread adoption of electronic health records (EHRs) facilitates seamless data exchange and improves the overall efficiency of medical coding and billing processes.



    The regulatory landscape is another critical factor influencing market growth. Governments and healthcare regulatory bodies across various regions have implemented stringent guidelines for medical coding and billing to combat fraud and abuse in the healthcare system. Compliance with these regulations necessitates the use of specialized coding and billing services, thereby driving market demand. Additionally, the increasing complexity of medical coding systems, such as the transition from ICD-9 to ICD-10, requires skilled professionals to ensure accurate and compliant coding, further boosting market growth.



    Regionally, North America holds the largest share of the medical coding and billing services market, primarily due to the high adoption of advanced healthcare IT solutions and the presence of well-established healthcare infrastructure. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by increasing healthcare expenditures, growing awareness about the benefits of professional coding and billing services, and the rapid digitization of healthcare systems. Europe also represents a significant market, with substantial investments in healthcare IT and stringent regulatory frameworks.



    Healthcare BPO Services have emerged as a pivotal component in the healthcare industry, particularly in the realm of medical coding and billing. These services offer healthcare providers the flexibility to outsource non-core functions, allowing them to concentrate on delivering quality patient care. By leveraging the expertise of specialized BPO providers, healthcare organizations can ensure compliance with regulatory standards, reduce operational costs, and enhance the accuracy of their billing processes. The integration of advanced technologies, such as AI and ML, within BPO services further optimizes efficiency and minimizes errors, making it an attractive option for healthcare providers seeking to streamline their operations.



    Service Type Analysis



    The medical coding and billing services market is segmented by service type into coding, billing, and others. The coding segment involves the translation of healthcare diagnoses, procedures, medical services, and equipment into universal medical alphanumeric codes. This segment is critical for ensuring that healthcare providers are reimbursed accurately and promptly for the services rendered. The demand for skilled medical coders is on the rise due to the complex nature of coding systems and the need for accuracy in reporting patient data. Ad

  13. U

    United States Health Insurance: Accident and Health: Net Incurred Claims:...

    • ceicdata.com
    + more versions
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    CEICdata.com, United States Health Insurance: Accident and Health: Net Incurred Claims: Accident Only or Accidental Death and Dismemberment [Dataset]. https://www.ceicdata.com/en/united-states/health-insurance-accident-and-health-net-incurred-claims-by-lines-of-business/health-insurance-accident-and-health-net-incurred-claims-accident-only-or-accidental-death-and-dismemberment
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2015 - Dec 1, 2023
    Area covered
    United States
    Variables measured
    Insurance Market
    Description

    United States Health Insurance: Accident and Health: Net Incurred Claims: Accident Only or Accidental Death and Dismemberment data was reported at 2.995 USD bn in 2023. This records an increase from the previous number of 2.630 USD bn for 2022. United States Health Insurance: Accident and Health: Net Incurred Claims: Accident Only or Accidental Death and Dismemberment data is updated yearly, averaging 2.640 USD bn from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 3.006 USD bn in 2018 and a record low of 2.339 USD bn in 2021. United States Health Insurance: Accident and Health: Net Incurred Claims: Accident Only or Accidental Death and Dismemberment 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.RG020: Health Insurance: Accident and Health: Net Incurred Claims by Lines of Business.

  14. c

    Insurance Claims Management Solution market will grow at a cagr of 6.09%...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
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    Cognitive Market Research, Insurance Claims Management Solution market will grow at a cagr of 6.09% from 2024 to 2031 [Dataset]. https://www.cognitivemarketresearch.com/insurance-claims-management-solution-market-report
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    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    As per Cognitive Market Research's latest published report, the Global Insurance Claims Management Solution market size is $15,764.89 Billion in 2024 and it is forecasted to reach $23,846.29 Billion by 2031. Insurance Claims Management Solution Industry's Compound Annual Growth Rate will be 6.09% from 2024 to 2031. Market Dynamics of the Insurance Claims Management Solution Market

    Market Drivers of the Insurance Claims Management Solution Market

    Growing Health Insurance Claims is Increasing the Demand for Global Insurance Claims Management Solution Market.
    

    The increasing number of health insurance claims made globally is the main factor driving the growth of the global insurance claims management solutions market. The frequency and complexity of insurance claims in the healthcare industry have significantly increased in recent years, making the use of smart and effective claims management systems necessary. For instance, compared to July 2021, when growth was 9.9%, healthcare insurance increased by 34.2% year-to-date as of July 2022. A number of factors, such as the aging population, the increasing frequency of chronic diseases, and improved knowledge and accessibility to healthcare services, are responsible for this rise in health insurance claims.

    In 2022, 90.3% of the population was covered by health insurance, a rise from 89.0% in 2021. (Source: United States Census Bureau). Adults in the middle age groups were the main drivers of this growth. There has been an apparent shift in disease trends as the world's population ages, with a rise in the prevalence of chronic illnesses that require protracted and intensive medical care. Health insurance claims have increased significantly as a result of this demographic trend, putting an immense amount of pressure on insurance companies to speed up their claims processing procedures. Effective claims management systems are critical to the insurance industry because of the complex nature of healthcare services and the requirement for accurate documentation and quick processing. In addition, spending on private health insurance increased 5.9% in 2022 to $1,289.8 billion.

    Increasing Demand for Faster Insurance Claims by Individuals is Driving the Demand for Insurance Claims Management Solution Market.
    

    Shifting customer expectations are driving an enormous shift in the insurance sector. The growing need for quicker and more effective insurance claim processing is one of the main factors driving how the insurance services industry is changing. People anticipate the same degree of efficiency and speed when it comes to insurance claims as they grow increasingly proficient in technology and used to ideal digital interactions in many areas of their life. For instance, by 2025, it's possible that 90% of contacts with insurance customers will be digital.

    Customers of insurance prioritize quick claims processing and reimbursements. Approximately 66% of insurance clients want immediate feedback while their claims are processed. In addition, it is essential to make sure that consumers can update contact information, modify their coverage, read policy papers, begin and monitor claims online, as indicated by the 77% of auto insurance customers who prefer digital claims procedures. The insurance sector has experienced a sharp increase in the use of insurance claims management systems in response to this expanding demand. These solutions use modern technologies like automation, machine learning, and artificial intelligence to speed up and simplify the claims procedure. Insurance claims have historically been seen as a long and difficult part of the insurance process, sometimes including extensive paperwork, manual evaluations, and delayed reimbursements. In order to solve these issues and improve the client experience as a whole, there has been a trend towards digitalization and the integration of advanced claims management tools.

    In addition to customer expectations, evolving risks and catastrophes also have an impact on the requirement for speedier insurance claims processing. Processing an increasing number of claims quickly is a difficulty for the insurance sector due to the emergence of new and complicated hazards like cyber-attacks and natural catastrophes. Solutions for insurance claims management are essential for addressing these many and changing problems because they give insurers the means to quickly eva...

  15. Group health insurance claim ratio in India FY 2018-2023

    • statista.com
    Updated Apr 30, 2025
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    Statista (2025). Group health insurance claim ratio in India FY 2018-2023 [Dataset]. https://www.statista.com/statistics/1611096/india-group-business-health-insurance-claims-ratio/
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    Dataset updated
    Apr 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    In the financial year 2023, the group health insurance claim ratio reached 97 percent in India. However, the claim ratio for group health insurance was the highest at 119 percent for the previous year due to the COVID-19 pandemic.

  16. United States Health Insurance: Claims Adjustment Expenses: Other

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Health Insurance: Claims Adjustment Expenses: Other [Dataset]. https://www.ceicdata.com/en/united-states/health-insurance-operations-by-lines-of-business/health-insurance-claims-adjustment-expenses-other
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2012 - Dec 1, 2023
    Area covered
    United States
    Variables measured
    Insurance Market
    Description

    United States Health Insurance: Claims Adjustment Expenses: Other data was reported at 1.000 USD bn in 2023. This records a decrease from the previous number of 1.608 USD bn for 2022. United States Health Insurance: Claims Adjustment Expenses: Other data is updated yearly, averaging 778.000 USD mn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 1.608 USD bn in 2022 and a record low of 237.000 USD mn in 2007. United States Health Insurance: Claims Adjustment Expenses: Other 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.

  17. D

    Health Insurance Platforms Market Report | Global Forecast From 2025 To 2033...

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Health Insurance Platforms Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-health-insurance-platforms-market
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    pptx, pdf, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Health Insurance Platforms Market Outlook



    The global health insurance platforms market size is projected to grow significantly, with estimates suggesting a value of approximately $85 billion in 2023, expanding to around $155 billion by 2032, reflecting a robust CAGR of 7.1%. This growth is primarily fueled by the increasing demand for efficient healthcare management solutions and the rising penetration of digital technologies in the healthcare sector. Factors such as the escalating costs of healthcare services, growing awareness about health insurance benefits, and the need for seamless integration of various healthcare services are driving the expansion of this market. The adoption of advanced technologies such as AI and machine learning to enhance customer experience and streamline operations is also contributing to the market's growth trajectory.



    One of the primary growth factors for the health insurance platforms market is the increasing complexity of healthcare systems worldwide. As healthcare delivery models evolve, there is a corresponding need for sophisticated health insurance platforms that can manage multiple aspects of healthcare services, from patient data to billing and claims processing. These platforms are designed to provide comprehensive solutions that improve efficiency and reduce errors in the processing of health insurance claims. Moreover, the ability of these platforms to integrate with existing healthcare IT infrastructure, offering seamless interoperability and data exchange, positions them as indispensable tools for both healthcare providers and insurers.



    Additionally, the growing prevalence of lifestyle-related diseases and chronic conditions is compelling individuals and families to invest more in health insurance policies, thereby driving demand for more robust and user-friendly health insurance platforms. The increasing consumer awareness regarding the benefits of health insurance is further propelling this demand. Health insurance platforms enable insurers to offer personalized insurance plans, tailored to meet the specific needs of individuals and families, while also providing tools for policy management and premium calculation. This capability is crucial in attracting and retaining customers in an increasingly competitive market.



    The rise of digital healthcare solutions and telemedicine is another significant factor contributing to the growth of the health insurance platforms market. As more healthcare services move online, there is a pressing need for insurance platforms that can adapt to these new service delivery models. Health insurance platforms are now incorporating features such as telehealth integration, real-time data analytics, and virtual health consultations, which enhance the value proposition of health insurance plans. This digital transformation is not only streamlining operations but also improving the accessibility and affordability of healthcare services across the globe.



    Healthcare Insurance plays a pivotal role in the health insurance platforms market, providing individuals and families with financial protection against high medical costs. As healthcare expenses continue to rise, more people are recognizing the importance of having a reliable healthcare insurance plan. These plans not only cover routine check-ups and preventive care but also provide coverage for unexpected medical emergencies, surgeries, and hospital stays. The integration of digital technologies into healthcare insurance platforms is enhancing the customer experience by offering features such as online policy management, claims processing, and access to a wide network of healthcare providers. This digital shift is making healthcare insurance more accessible and user-friendly, encouraging more people to invest in comprehensive coverage.



    From a regional perspective, North America remains a dominant player in the health insurance platforms market, owing to the advanced healthcare infrastructure and the high adoption rate of digital solutions in this region. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period, driven by the rapid digitalization of healthcare services and the increasing demand for affordable health insurance options. Europe also presents significant growth opportunities, with a strong focus on improving healthcare accessibility and efficiency through technological advancements.



    Component Analysis



    The health insurance platforms market is majorly segmented by compo

  18. Forecast: Gross Incurred Claims of Health Insurance in Germany 2024 - 2028

    • reportlinker.com
    Updated Apr 11, 2024
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    ReportLinker (2024). Forecast: Gross Incurred Claims of Health Insurance in Germany 2024 - 2028 [Dataset]. https://www.reportlinker.com/dataset/7fe1105800e096799b2e8f4538132be658d29421
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    Dataset updated
    Apr 11, 2024
    Dataset authored and provided by
    ReportLinker
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Germany
    Description

    Forecast: Gross Incurred Claims of Health Insurance in Germany 2024 - 2028 Discover more data with ReportLinker!

  19. M

    Medical Insurance Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Mar 5, 2025
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    Archive Market Research (2025). Medical Insurance Report [Dataset]. https://www.archivemarketresearch.com/reports/medical-insurance-48545
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    doc, ppt, pdfAvailable download formats
    Dataset updated
    Mar 5, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global medical insurance market is experiencing robust growth, driven by factors such as rising healthcare costs, increasing prevalence of chronic diseases, and expanding health insurance coverage globally. The market size in 2025 is estimated at $2.5 trillion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This substantial growth is fueled by several key trends, including the increasing adoption of telehealth and digital health solutions, the rise of value-based care models, and the growing demand for supplemental health insurance products. Furthermore, government initiatives promoting health insurance coverage in developing economies significantly contribute to market expansion. However, challenges such as regulatory complexities, high administrative costs associated with claims processing, and the rising burden of medical fraud and abuse act as restraints on market growth. Segmentation analysis reveals significant opportunities across various application areas. The large insurance segment dominates, accounting for a major portion of the market share due to its extensive reach and comprehensive coverage options. However, the microinsurance segment is witnessing rapid growth, particularly in emerging markets, catering to the underserved populations with affordable and accessible healthcare solutions. Similarly, the insured liability type demonstrates high growth potential due to the increasing concerns surrounding medical malpractice and professional liability. Payment method segmentation shows a growing preference for digital payment options, reflecting global trends in digitalization and convenience. Key players in the medical insurance market include established global giants like Chubb, AIG, and Allianz, as well as regional players focusing on specific niche segments. Competitive landscape analysis highlights the strategic mergers and acquisitions, partnerships, and technological advancements driving the market dynamics. The forecast period from 2025 to 2033 indicates continued strong growth, with the market expected to reach approximately $4.5 trillion by 2033.

  20. f

    Comparative effectiveness of generic and brand-name medication use: A...

    • figshare.com
    pdf
    Updated Mar 13, 2019
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    Rishi J. Desai; Ameet Sarpatwari; Sara Dejene; Nazleen F. Khan; Joyce Lii; James R. Rogers; Sarah K. Dutcher; Saeid Raofi; Justin Bohn; John G. Connolly; Michael A. Fischer; Aaron S. Kesselheim; Joshua J. Gagne (2019). Comparative effectiveness of generic and brand-name medication use: A database study of US health insurance claims [Dataset]. http://doi.org/10.1371/journal.pmed.1002763
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    pdfAvailable download formats
    Dataset updated
    Mar 13, 2019
    Dataset provided by
    PLOS Medicine
    Authors
    Rishi J. Desai; Ameet Sarpatwari; Sara Dejene; Nazleen F. Khan; Joyce Lii; James R. Rogers; Sarah K. Dutcher; Saeid Raofi; Justin Bohn; John G. Connolly; Michael A. Fischer; Aaron S. Kesselheim; Joshua J. Gagne
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundTo the extent that outcomes are mediated through negative perceptions of generics (the nocebo effect), observational studies comparing brand-name and generic drugs are susceptible to bias favoring the brand-name drugs. We used authorized generic (AG) products, which are identical in composition and appearance to brand-name products but are marketed as generics, as a control group to address this bias in an evaluation aiming to compare the effectiveness of generic versus brand medications.Methods and findingsFor commercial health insurance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Clinformatics Data Mart (years: 2004–2013) and (2) Truven MarketScan (years: 2003–2015). For a total of 8 drug products, the following groups were compared using a cohort study design: (1) patients switching from brand-name products to AGs versus generics, and patients initiating treatment with AGs versus generics, where AG use proxied brand-name use, addressing negative perception bias, and (2) patients initiating generic versus brand-name products (bias-prone direct comparison) and patients initiating AG versus brand-name products (negative control). Using Cox proportional hazards regression after 1:1 propensity-score matching, we compared a composite cardiovascular endpoint (for amlodipine, amlodipine-benazepril, and quinapril), non-vertebral fracture (for alendronate and calcitonin), psychiatric hospitalization rate (for sertraline and escitalopram), and insulin initiation (for glipizide) between the groups. Inverse variance meta-analytic methods were used to pool adjusted hazard ratios (HRs) for each comparison between the 2 databases. Across 8 products, 2,264,774 matched pairs of patients were included in the comparisons of AGs versus generics. A majority (12 out of 16) of the clinical endpoint estimates showed similar outcomes between AGs and generics. Among the other 4 estimates that did have significantly different outcomes, 3 suggested improved outcomes with generics and 1 favored AGs (patients switching from amlodipine brand-name: HR [95% CI] 0.92 [0.88–0.97]). The comparison between generic and brand-name initiators involved 1,313,161 matched pairs, and no differences in outcomes were noted for alendronate, calcitonin, glipizide, or quinapril. We observed a lower risk of the composite cardiovascular endpoint with generics versus brand-name products for amlodipine and amlodipine-benazepril (HR [95% CI]: 0.91 [0.84–0.99] and 0.84 [0.76–0.94], respectively). For escitalopram and sertraline, we observed higher rates of psychiatric hospitalizations with generics (HR [95% CI]: 1.05 [1.01–1.10] and 1.07 [1.01–1.14], respectively). The negative control comparisons also indicated potentially higher rates of similar magnitude with AG compared to brand-name initiation for escitalopram and sertraline (HR [95% CI]: 1.06 [0.98–1.13] and 1.11 [1.05–1.18], respectively), suggesting that the differences observed between brand and generic users in these outcomes are likely explained by either residual confounding or generic perception bias. Limitations of this study include potential residual confounding due to the unavailability of certain clinical parameters in administrative claims data and the inability to evaluate surrogate outcomes, such as immediate changes in blood pressure, upon switching from brand products to generics.ConclusionsIn this study, we observed that use of generics was associated with comparable clinical outcomes to use of brand-name products. These results could help in promoting educational interventions aimed at increasing patient and provider confidence in the ability of generic medicines to manage chronic diseases.

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Beenu Sharma (2022). Fraudlent claim in healthcare [Dataset]. https://www.kaggle.com/datasets/beenusharma42/fraudlent-claim-in-healthcare
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Fraudlent claim in healthcare

fraud detection in healthcare

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 20, 2022
Dataset provided by
Kaggle
Authors
Beenu Sharma
Description

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.

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