NY Property & Casualty Insurance Premiums Written 1998-2022

Benchmarking Insurer Performance

By State of New York [source]

About this dataset

This dataset contains total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Listings include essential financial security requirements that are required by Article 41 of the New York Insurance Law and provide insights into how the industry has evolved over time. This is an invaluable resource for researchers, analysts, policy makers, and insurance agents alike who wish to better understand the changing dynamics of the insurance market in New York. Download now and explore this unique dataset detailing net premiums written for insurers over a 20+ year period

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How to use the dataset

This dataset contains the total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Using this dataset, users can explore total property insurance premiums written over the course of twenty-four years in order to gain an understanding of the property insurance industry trends in New York State.

To use this dataset effectively, first download and read the Terms of Service before using the data. Once familiar with how to leverage data licenses effectively, you can analyze or visualize various facets of this large dataset. You may be interested in seeing changes over time and can compare these values with national averages or Gross Domestic Product (GDP) figures for periods analyzed.

Additionally, you could study any variation by geographic areas or other variables such as age groupings or type of policy written during a certain period. This dataset provides comprehensive insights that allow you to look at macro levels (loose overview) as well as more granular views depending on your questions and analysis methods. Regardless of your specific analysis goals; utilization of this open source data set should yield valuable insight into past trends which have potential impacts on future activities related to property and casualty insurance policies within New York State!

Research Ideas

• Identifying trends in Property & Casualty insurance rates over time in New York State to inform consumer decision making or policy strategies.
• Developing a risk management model by analyzing the financial security requirements of insurers in New York State and predicting potential premiums on different types of coverage areas.
• Comparing different insurers on their total net premiums written to compare their relative market size and influence within the state’s property & casualty insurance industry

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: total-property-insurance-premiums-written-annually-in-new-york-beginning-1998-1.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------| | Net Premiums Written | The total amount of premiums written by the insurer in thousands. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

NY Insurance Policy Assets, Liabilities, and Premiums Annually

Investigating the Impact of Financial Health on Health Insurance Costs

By State of New York [source]

About this dataset

This dataset tracks health insurance premiums written in New York annually since 2004. It provides vital insight into the amount of money and risk taken on by insurance companies in the state: including what types of insurers are writing policies, how much they are taking on in assets and liabilities, and how this has shifted over time. This data will be invaluable to those looking to understand large scale trends in terms of the health insurance industry. The data has been updated as recently as 2021, so it provides a comprehensive picture of changes year-over-year spanning nearly two decades

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How to use the dataset

This dataset contains vital information regarding health insurance premiums, assets and liabilities related to policies written in New York annually. It is designed to provide key insights into the performance of insurance companies in New York state.

The data consists of Type of Insurer, Company Name, Year, Assets, Liabilities and Premium Written for each policy written in every year since 2009. This data can be used to gain greater insight into the performance of certain companies within this industry over time as well as creating benchmarked comparison metrics against other companies within this market space.

For individual or team exploration projects – you may want to compare one company’s yearly assets/liabilities or premiums against the average value for that same period in order to identify high or low performing periods or take a look at how some variables changed across a 5 year (or wider) timescale e.g compare how did assets/liabilites changed over the duration of 5 years?

By utilizing basic data visualizations like scatterplots and bar graphs we can start gaining more insights from our analysis by looking at potential correlations between variables such as: Are premium prices related to their assets? Does company size have an impact on the premium price? Have liabilities remained constant compared with past years?

Administrators in management roles could also use this dataset to track yearly changes within their own companys results- such as tracking existing trends over longer periods with pay attention for changes which require further investigation/ research as necessary .

All in all this data set is a great tool for students , researchers & analysts alike!

Research Ideas

• Establishing a baseline of average health insurance premiums in New York by year across different insurers.
• Comparing insurance company assets and liabilities with their premium-written to provide an understanding of how profitable they are in the New York market.
• Tracking the growth and success of health insurers in the New York over time to understand changes in industry trends or policy standards

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-insurance-premiums-on-policies-written-in-new-york-annually-1.csv | Column name | Description | |:--------------------|:--------------------------------------------------------------------------------------------------------------------------------| | Type of Insurer | This column indicates the type of insurer that wrote the policy. (String) | | Company Name | This column indicates the name of the company that wrote the policy. (String) | | Year | This column indicates the year that the policy was written in. (Integer) | | Assets | This column indicates the total assets of the company that wrote the policy. (Integer) | | Liabilities | This column indicates the total liabilities of the company that wrote the policy. (Integer) | | Premium Written | This column indicates the total amount paid by an individual or organization for a given product or service annually. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

Snapshot img

Insurance Analytics Market 2024-2028

The insurance analytics market size is projected to increase by USD 13.14 billion, at a CAGR of 15.96% between 2023 and 2028. The growth rate of the market depends on several factors, including the increasing government regulations on mandatory insurance coverage in developing countries, the increasing availability of big data tools, and the growing need for insurers to make data-driven decisions. Insurance analytics involves the use of data analysis and statistical techniques to gain insights into the insurance industry. It helps insurers make informed decisions, assess risks, detect fraudulent activities, and enhance overall operational efficiency. This technology leverages data from various sources, including customer information, claims data, and market trends, to optimize underwriting, pricing, and claims processing activities.

The report includes a comprehensive outlook on the Insurance Analytics Market, offering forecasts for the industry segmented by Deployment, which comprises cloud and on-premises. Additionally, it categorizes Component into tools and services and covers Regions, including North America, Europe, APAC, Middle East and Africa, and South America. The report provides market size, historical data spanning from 2018 to 2022, and future projections, all presented in terms of value in USD billion for each of the mentioned segments.

What will be the size of the Insurance Analytics Market During the Forecast Period?

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Insurance Analytics Market Overview

Insurance Analytics Market Driver

Increasing government regulations on mandatory insurance coverage in developing countries is the key factor driving market growth. Third-party motor insurance is compulsory for vehicles that run on public roads in some countries. For example, anyone who owns or operates a vehicle in the state of Maine in the US must have at least the minimum amount of insurance required by law. Similarly, health insurance is mandatory in most developed countries. Travel insurance is mandatory for a person traveling to a foreign country (in most developed countries).

Furthermore, the travel Insurance industry is expected to grow at a rapid pace due to the increase in cross-country tourism. The health insurance analytics industry is growing slowly in developing countries because of the increased awareness about the importance of having health insurance. As a result, the growth of various types of insurance is resulting in the rapid expansion of the global insurance analytics market.

Insurance Analytics Market Trends

Increasing adoption of insurance in developing countries is the primary trend shaping market growth. The market is currently expanding at a fast pace because of the increasing awareness about the importance of insurance. Emerging markets, mainly China and India, are expected to contribute to the rapid growth of the insurance industry.

In addition, the digital transformation in the insurance industry has resulted in a rapid increase in the demand for upgraded customer-facing insurance analytics solutions. With the increasing demand for insurance in developing countries, the demand for insurance analytics is also growing at a fast pace. Traditional methods of insurance are not favored anymore.

Insurance Analytics Market Restrain

The complexity of integrating diverse data sources is a challenge that affects market growth. Insurers often deal with vast amounts of data generated by various channels, and integrating this data seamlessly can be complex and complicated. Standardizing data formats, ensuring data quality, and establishing interoperability between different systems are crucial aspects. Overcoming these integration challenges is essential for insurers to harness the full potential of analytics and derive meaningful insights from the diverse datasets available to them.

Furthermore, the insurance sector is a heavily regulated industry, and data use and integration must comply with various regional and industry-specific regulations. Ensuring adherence to compliance standards adds complexity to the overall integration process. In addition, inaccuracies or inconsistencies can lead to flawed insights and decisions.

Insurance Analytics Market Segmentation By Deployment

The market share growth by the cloud segment will be significant during the forecast period. Cloud-based insurance analytics refers to the use of cloud computing services to store, analyze, and process insurance-related data. By leveraging cloud platforms, insurers can benefit from enhanced scalability, flexibility, and accessibility. This enables the efficient handling of large datasets, faster analytics processing, and the ability to access insights from virtually anywhere.

Get a glance at the market contribution of various segments Download the PDF Sam

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...

According to Cognitive Market Research, the global Artificial Intelligence AI in Insurance market size is USD 4681.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 33.60% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 1872.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 31.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 1404.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 1076.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.6% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 234.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 33.0% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 93.62 million in 2024 and will grow at a compound annual growth rate (CAGR) of 33.3% from 2024 to 2031.
The Hardware held the highest Artificial Intelligence AI in Insurance market revenue share in 2024.

Market Dynamics of Artificial Intelligence AI in Insurance Market

Key Drivers of Artificial Intelligence AI in Insurance Market

Data Explosion and Processing Power to Increase the Demand Globally

The proliferation of data and advances in processing capacity are causing a revolution in the insurance sector. Insurance companies must overcome the difficulty of efficiently evaluating and utilizing the massive volumes of data that are being collected, which range from driving patterns to client demographics. The ability of artificial intelligence (AI), which can analyze data more accurately and quickly than humans, makes it an important answer. Insurance companies may make better judgments about risk assessment, pricing, and personalized offerings by using AI algorithms to extract insightful information from large, complicated datasets. This improves operational effectiveness and consumer happiness.

Improved Risk Assessment and Underwriting to Propel Market Growth

The insurance business collects data, including a wide range of information, including driving habits and client demographics. By dramatically improving data processing capabilities, artificial intelligence (AI) offers a disruptive possibility. Insurers can quickly and accurately extract useful insights from complicated datasets with unprecedented speed and precision using AI analysis. Thanks to this increased efficiency, Insurance companies can make faster, more informed decisions—from risk assessment to customized policy offerings. Insurance companies can improve operational efficiency, effectively manage risks, and ultimately offer more individualized services to their clients by utilizing AI's capacity to navigate the data explosion. This will help the industry become more adaptable and resilient to changing market conditions.

Restraint Factors Of Artificial Intelligence AI in Insurance Market

Rising Risk Assessment to Limit the Sales

Using sophisticated data analytics, AI algorithms are transforming risk assessment and underwriting in the insurance sector. These algorithms are highly skilled at analyzing complex datasets to identify trends and predict dangers with previously unheard-of accuracy. Insurers can increase customer satisfaction and loyalty by providing low-risk customers with more competitive rates when they are reliably identified as such. Furthermore, insurers can quickly and efficiently identify possible fraudulent activity due to AI's skill in detecting anomalies. Insurance companies benefit from streamlined underwriting procedures, reduced losses, and increased profitability due to improved risk assessment and fraud detection. AI technologies improve the insurance sector's capacity to customize policies, reduce risks, and stop fraudulent activity, creating a more robust and customer-focused market.

Impact of COVID-19 on the Artificial Intelligence AI in the Insurance Market

Artificial Intelligence (AI) in the insurance industry has been greatly impacted by the COVID-19 epidemic, creating both potential and challenges. The crisis highlighted the significance of artificial intelligence (AI) in insurance, even as it slowed down conventional...

Snapshot img

Insurance Software Market Size 2025-2029

The insurance software market size is forecast to increase by USD 9.87 billion, at a CAGR of 9.3% between 2024 and 2029.

The market is experiencing significant growth and transformation, driven by increasing government regulations mandating insurance coverage in developing countries and the integration of wearables into customer engagement metrics for life insurance. These trends reflect a growing emphasis on risk management and personalized customer experiences. However, the market also faces challenges, including a tightening regulatory environment for insurance players. Compliance with evolving regulations is essential to maintain market position and mitigate potential penalties. Additionally, the integration of wearables presents opportunities for more accurate risk assessment and personalized pricing, but also raises concerns around data privacy and security.
To capitalize on market opportunities and navigate challenges effectively, insurance providers must stay informed of regulatory changes and invest in robust data security measures. By embracing technology and adapting to regulatory requirements, insurers can enhance their offerings and build stronger relationships with customers.

What will be the Size of the Insurance Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities shaping its landscape. Entities reporting and analytics, user experience (UX), regulatory reporting, integration APIs, database management, machine learning (ML), data security, cloud computing, data privacy, sales management, and various other components are increasingly integrated to offer comprehensive solutions. Policy issuance, customer portals, document management, and broker management are seamlessly integrated into the policy lifecycle, enabling efficient and effective operations. Predictive analytics, microservices architecture, and agile development are transforming the industry, allowing insurers to make data-driven decisions and respond quickly to market trends. User interface (UI) and mobile applications are essential for enhancing the customer experience, while API integrations and sales force automation streamline internal processes.

Actuarial modeling, billing systems, quality assurance (QA), commission management, and premium calculation are crucial for accurate risk assessment and pricing. Data analytics, claims management, reporting & analytics, and machine learning (ML) are at the forefront of innovation, enabling insurers to detect fraud, process claims efficiently, and gain valuable insights from vast amounts of data. Data security, cloud computing, and data privacy are paramount in ensuring the protection of sensitive information. The ongoing evolution of the market reflects the industry's commitment to meeting the ever-changing needs of customers and regulatory requirements. The integration of these advanced technologies and processes will continue to reshape the market, offering new opportunities for growth and efficiency.

How is this Insurance Software Industry segmented?

The insurance software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based


Type

  Life insurance
  Accident and health insurance
  Property and casualty insurance
  Others


End-user

  Insurance companies
  Agencies
  Brokers


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the adoption of advanced technologies such as predictive analytics, microservices architecture, and artificial intelligence (AI) in policy administration, claims management, and risk management. Customer portals and document management systems facilitate seamless interaction between insurers and policyholders, enhancing the user experience (UX). Policy issuance and renewal management are streamlined through API integrations and agile development, enabling real-time processing. Mobility is a key trend, with insurers developing mobile applications to cater to the growing demand for on-the-go access to insurance services. Data analytics and regulatory reporting are essential components, ensuring compliance with industry regulations and providing valuable insights for strategic decision-making.

Policy lifecycle

Insurance Rating Software Market size was valued at USD 368 Million in 2023 and is projected to reach USD 667.6 Million by 2030, growing at a CAGR of 8.7% during the forecast period 2024-2030.

Global Insurance Rating Software Market Drivers

The market drivers for the Insurance Rating Software Market can be influenced by various factors. These may include:

Technological Progress: Insurance rating software can be made more accurate and efficient by incorporating machine learning (ML) and artificial intelligence (AI), among other ongoing technical breakthroughs.

Adherence to Regulations: The use of updated rating software to ensure compliance may be prompted by changes in regulatory requirements within the insurance business. These modifications could involve modifying risk assessment techniques or introducing new reporting requirements.

Big Data and Data Analytics: Big data analytics is becoming more widely available and used, which enables insurance businesses to make better decisions. Large dataset processing and analysis capabilities are likely to make insurance rating software in high demand.

Experience of the Customer and Customisation: Insurance companies are putting more of an emphasis on providing individualised products and enhancing the client experience. This objective can be attained in part by using insurance rating software that permits more accurate risk assessment for specific policyholders.

Market expansion and globalisation: The requirement for rating software that can adjust to various markets and regulatory contexts grows as insurance companies expand their operations worldwide.

Issues with cybersecurity: The increased dependence on digital platforms and data has made insurance rating software cybersecurity protocols essential. A cybersecurity-focused software solution is likely to be well-received by customers.

Economy of Cost: Insurance firms are constantly searching for methods to cut expenses and simplify their processes. One important motivator could be rating software that improves underwriting and risk assessment efficiency.

Collaborations & Partnerships: Advanced insurance rating software can be developed and adopted more quickly through partnerships within the insurtech ecosystem and through collaborations between technology providers and insurance firms.

Market Rivalry: The insurance industry's competitive environment may encourage businesses to invest in technology that gives them a competitive advantage. Innovative features in insurance rating software can draw in more users.

Environmental Elements: More advanced insurance rating techniques will be needed as a result of factors that can affect risk assessment models, such as variations in weather patterns, natural disasters, and other environmental variables.

Data Analytics in L & H Insurance Market Outlook

The global Data Analytics in Life and Health (L & H) Insurance market size was valued at approximately USD 5.25 billion in 2023 and is projected to reach USD 15.54 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.8% from 2024 to 2032. The expansion of data analytics in the L & H insurance market is driven by the increasing need for enhanced risk management, fraud detection, and customer service capabilities facilitated by advanced data analysis techniques.

The growth of data analytics in the L & H insurance market is primarily fueled by the rising adoption of digital technologies within the insurance sector. As insurance companies increasingly digitize their operations, the availability of vast amounts of data has enabled the application of sophisticated analytics to derive actionable insights. This, in turn, allows insurers to better understand customer behavior, accurately assess risks, and enhance the overall efficiency of their operations. Additionally, the growing incidence of fraudulent claims has necessitated the deployment of robust data analytics tools to detect and prevent fraud, thereby reducing financial losses.

Another significant growth factor is the advent of artificial intelligence (AI) and machine learning (ML) technologies. These technologies have revolutionized data processing capabilities, enabling insurers to analyze complex datasets rapidly and with greater precision. AI and ML algorithms can identify patterns and trends that traditional analytics methods might miss, providing insurers with a deeper understanding of risk factors and customer needs. This technological advancement is particularly beneficial in the areas of underwriting and claims management, where timely and accurate decision-making is crucial.

Moreover, the increasing consumer demand for personalized insurance products has pushed insurers to leverage data analytics to tailor their offerings. By analyzing customer data, insurers can create customized policies that meet the specific needs of individual clients, enhancing customer satisfaction and retention. This trend is further supported by the widespread use of wearable devices and health apps, which provide real-time health data that can be used to offer personalized health insurance plans and wellness programs. The integration of these technologies into insurance products not only improves customer engagement but also promotes preventive healthcare.

Regionally, North America is expected to dominate the market due to the high adoption rate of advanced technologies and the presence of major insurance companies investing in data analytics solutions. The Asia Pacific region is anticipated to witness significant growth, driven by the rapid digital transformation in emerging economies and increasing insurance penetration. Europe follows closely, with a strong focus on regulatory compliance and the implementation of data-driven strategies to enhance customer experience and operational efficiency. Latin America and the Middle East & Africa regions are also expected to showcase substantial growth, albeit at a slower pace, as insurers in these regions gradually adopt data analytics technologies.

Component Analysis

In the realm of data analytics for L & H insurance, the market is segmented by component into software and services. Software solutions encompass a wide range of analytics tools and platforms designed to collect, process, and analyze insurance data. These solutions include predictive analytics, big data analytics, risk modeling, and customer analytics software. The adoption of such software is crucial for insurers aiming to gain a competitive edge through data-driven decision-making. The increasing complexity and volume of data generated by insurance operations necessitate the deployment of sophisticated software solutions capable of handling large datasets efficiently.

On the other hand, the services segment includes consulting, implementation, and support services provided by analytics vendors and third-party service providers. These services are essential for the successful deployment and maintenance of analytics solutions within insurance companies. Consulting services help insurers identify the right analytics tools and strategies to meet their specific needs, while implementation services ensure the seamless integration of these tools into existing systems. Support services provide ongoing assistance to address any technical issues and ensure the optimal performance of analytics solutions.

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

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Underwriting And Rating Software Market size was valued at USD 5.8 Billion in 2023 and is projected to reach USD 13.9 Billion by 2030, growing at a CAGR of 12.5% during the forecast period 2024-2030.

Global Underwriting And Rating Software Market Drivers Numerous elements propel the underwriting and rating software industry, fostering its expansion and development. Among the principal forces shaping the market are:

Efficiency and Automation: By automating intricate procedures, underwriting and rating software lowers manual labor and boosts overall productivity. One major factor driving the adoption of such software is the need for cost reductions and improved processes.

Data Analytics and Insights: Insurers can evaluate big datasets for risk by utilizing sophisticated analytics techniques that are incorporated into underwriting software. Data-driven insights increase the accuracy of underwriting, enabling insurers to make better decisions and manage risk.

Loss prevention and risk mitigation: Underwriting software aids insurers in more efficient risk assessment and mitigation. The capacity to recognize possible hazards and implement suitable underwriting protocols facilitates the mitigation of losses, hence augmenting the fiscal steadiness of insurance firms.

AI and ML Integration: Underwriting and rating procedures are improved by the combination of artificial intelligence (AI) and machine learning (ML) technologies. Predictive modeling, fraud detection, and real-time risk assessment are made possible by these technologies, which improves pricing and underwriting choices.

Regulatory Compliance: The rules governing the insurance sector are always changing. Insurance companies can maintain regulatory compliance by using underwriting and rating software, which makes sure underwriting procedures follow industry and legal guidelines.

Customer-Centric strategy: When it comes to underwriting, insurers are progressively moving toward a customer-centric strategy. Personalized underwriting judgments based on unique client profiles, preferences, and behaviors are made possible by sophisticated software, which increases customer happiness and loyalty.

Globalization of Insurance Operations: Underwriting and rating software that can adjust to various regulatory frameworks and market conditions is becoming more and more necessary as insurance businesses expand their operations internationally. This will help to facilitate worldwide business growth.

Growth of Insurtech Solutions: The insurance business has experienced a surge in innovation due to the rise of insurtech (insurance technology) firms and solutions. One of the main forces behind the adoption of contemporary technology and methods in insurtech is the development of underwriting and rating software.

Demand for Real-Time Processing: In order to react swiftly to shifting consumer demands and market situations, insurers are looking for real-time underwriting capabilities. Insurance companies may maintain their competitiveness and agility by utilizing underwriting and rating technologies that can handle data in real time.

Cross-Industry Collaboration: The development of sophisticated underwriting solutions is fueled by partnerships between insurance companies and other sectors of the economy, including technology and data analytics. These collaborations aid in the development of rating and underwriting tools.

Growth of the Cyber Insurance Market: As cyber dangers become more serious, so does the need for cyber insurance. To support the expanding cyber insurance business, underwriting software with a focus on evaluating and pricing cyber risks is in high demand.

Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Financial and Insurance Activities data was reported at 1.351 USD bn in 2023. This records an increase from the previous number of 822.379 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Financial and Insurance Activities data is updated yearly, averaging 388.615 USD mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 1.351 USD bn in 2023 and a record low of -1.131 USD bn in 2009. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Financial and Insurance Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Income: Outward: USD: Total: Financial and Insurance Activities data was reported at 66.955 USD mn in 2023. This records an increase from the previous number of 38.198 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Outward: USD: Total: Financial and Insurance Activities data is updated yearly, averaging 1.860 USD mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 66.955 USD mn in 2023 and a record low of -8.046 USD mn in 2020. Lithuania LT: Foreign Direct Investment Income: Outward: USD: Total: Financial and Insurance Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Activities Other Than Financial and Insurance data was reported at 2.969 USD bn in 2023. This records a decrease from the previous number of 3.085 USD bn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Activities Other Than Financial and Insurance data is updated yearly, averaging 1.087 USD bn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 3.085 USD bn in 2022 and a record low of 64.865 USD mn in 2009. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Activities Other Than Financial and Insurance data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Cloud Database and DBaaS Market size was valued at USD 18.28 Billion in 2024 and is projected to reach USD 83.95 Billion by 2031, growing at a CAGR of 20.99% during the forecasted period 2024 to 2031.

The Cloud Database and Database as a Service (DBaaS) market is driven by the increasing adoption of cloud computing and big data analytics, as organizations seek scalable, flexible, and cost-effective data management solutions. The growing volume of unstructured data and the need for real-time data processing and analytics propel demand for cloud databases. Businesses' emphasis on reducing operational complexities and costs associated with traditional on-premise databases, coupled with the need for enhanced data security, disaster recovery, and compliance, further fuels market growth. The proliferation of IoT devices and the rise of AI and machine learning applications also contribute to the demand for robust cloud database solutions. Additionally, the trend towards digital transformation and the increasing reliance on remote work environments accentuate the need for reliable, accessible, and scalable database solutions.

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Big Data Security Market Size 2025-2029

The big data security market size is forecast to increase by USD 23.9 billion, at a CAGR of 15.7% between 2024 and 2029.

The market is driven by stringent regulations mandating data protection and an increasing focus on automation in big data security. With the growing volume and complexity of data, organizations are investing significantly in advanced security solutions to mitigate risks and ensure compliance. However, implementing these solutions comes with high financial requirements, posing a challenge for smaller businesses and budget-constrained organizations. Regulations, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), have intensified the need for robust data security measures. These regulations demand that organizations protect sensitive data from unauthorized access, use, or disclosure.
As a result, companies are investing in big data security solutions that offer advanced encryption, access control, and threat detection capabilities. Another trend in the market is the automation of big data security processes. With the increasing volume and velocity of data, manual security processes are no longer sufficient. Automation helps organizations to respond quickly to threats and maintain continuous security monitoring. However, the high cost of implementing and maintaining these automated solutions can be a significant challenge for many organizations. Intruders, ransomware attacks, unauthorized users, and other threats pose a constant risk to valuable information, intellectual property (IP), and transactional data.

What will be the Size of the Big Data Security Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the increasing volume and complexity of data being generated and collected across various sectors. Data governance is a critical aspect of this market, ensuring the secure handling and protection of valuable information. Blue teaming, a collaborative approach to cybersecurity, plays a crucial role in identifying and mitigating threats in real-time. Risk assessment and incident response are ongoing processes that help organizations prepare for and respond to data breaches. Security monitoring, powered by advanced technologies like AI in cybersecurity, plays a vital role in detecting and responding to threats. Data masking and anonymization are essential techniques for protecting sensitive data while maintaining its usability.

Network security, cloud security, and database security are key areas of focus, with ongoing threats requiring continuous vigilance. Threat intelligence and vulnerability management help organizations stay informed about potential risks and prioritize their response efforts. Disaster recovery and business continuity planning are also essential components of a robust security strategy. Cybersecurity insurance, security auditing, access control, penetration testing, and vulnerability scanning are additional services that help organizations fortify their defenses. Zero trust security and application security are emerging areas of focus, reflecting the evolving threat landscape. The market dynamics in this space are continuously unfolding, with new challenges and solutions emerging regularly.

How is this Big Data Security Industry segmented?

The big data security industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based


End-user

  Large enterprises
  SMEs


Solution

  Software
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The On-premises segment is estimated to witness significant growth during the forecast period. The market: Evolution and Trends in Enterprise Computing Big Data Security encompasses a range of technologies and practices designed to protect an organization's valuable data. Traditional on-premises servers form the backbone of many enterprise data infrastructures, with businesses owning and managing their hardware and software. These infrastructures include servers and storage units, located at secure sites, requiring specialized IT support for maintenance. Data security in this context is a top priority. Companies must establish user access policies, install firewalls and antivirus software, and apply security patches promptly. Network security is crucial, with vulnerability management and threat

Lithuania LT: Foreign Direct Investment Position: Outward: USD: Total: Activities Other Than Financial and Insurance data was reported at 11.392 USD bn in 2023. This records an increase from the previous number of 10.616 USD bn for 2022. Lithuania LT: Foreign Direct Investment Position: Outward: USD: Total: Activities Other Than Financial and Insurance data is updated yearly, averaging 2.884 USD bn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 11.392 USD bn in 2023 and a record low of 653.519 USD mn in 2005. Lithuania LT: Foreign Direct Investment Position: Outward: USD: Total: Activities Other Than Financial and Insurance data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Position: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.