Indonesia Insurance Statistics: Number of Registered Insurers: Life Insurers data was reported at 57.000 Unit in 2023. This records a decrease from the previous number of 59.000 Unit for 2022. Indonesia Insurance Statistics: Number of Registered Insurers: Life Insurers data is updated yearly, averaging 55.000 Unit from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 61.000 Unit in 2017 and a record low of 45.000 Unit in 2011. Indonesia Insurance Statistics: Number of Registered Insurers: Life Insurers data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA001: Insurance Statistics: Key Indicators.

Vector Database Vendor Liability Insurance Market Outlook

According to our latest research, the global Vector Database Vendor Liability Insurance market size reached USD 1.18 billion in 2024, reflecting the increasing demand for specialized risk coverage in the rapidly evolving data infrastructure landscape. The market is expanding at a robust CAGR of 14.6% and is forecasted to reach USD 3.55 billion by 2033. This significant growth is primarily driven by the escalating complexity of data management systems, heightened regulatory scrutiny, and the surge in cyber threats targeting database vendors. As organizations across industries increasingly rely on vector databases for advanced analytics and AI applications, the need for comprehensive liability insurance solutions has never been more critical.

A primary growth factor for the Vector Database Vendor Liability Insurance market is the exponential increase in data breaches and cyber-attacks targeting database infrastructure. With vector databases serving as the backbone for AI-driven analytics, natural language processing, and machine learning workloads, the consequences of a security lapse can be catastrophic. As a result, vendors are under immense pressure to ensure not only the integrity and availability of their platforms but also to manage the legal and financial risks associated with potential failures. Liability insurance has become an essential risk mitigation tool, offering protection against claims arising from data loss, unauthorized access, and operational disruptions. The growing sophistication of cyber threats and the frequency of high-profile incidents have further intensified the demand for tailored insurance products that address the unique exposures faced by vector database vendors.

Another critical driver is the rapidly evolving regulatory environment governing data privacy, security, and compliance. Legislation such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and similar frameworks in Asia Pacific and Latin America have placed stringent obligations on technology vendors to safeguard sensitive information. Non-compliance can result in significant penalties, reputational damage, and costly litigation. Consequently, liability insurance is increasingly viewed as a strategic investment by vendors seeking to navigate these regulatory complexities. Insurers are responding by developing innovative policies that cover a broad spectrum of risks, including regulatory fines, legal defense costs, and third-party liabilities, thereby enabling vendors to operate with greater confidence in global markets.

The surge in cloud adoption and the proliferation of hybrid and multi-cloud environments have also contributed to the expansion of the Vector Database Vendor Liability Insurance market. As organizations migrate mission-critical workloads to cloud-based vector databases, the risk landscape becomes more complex, encompassing issues such as shared responsibility models, data sovereignty, and cross-border data flows. Vendors must not only protect their own infrastructure but also address the risks associated with third-party service providers and integration partners. Liability insurance policies are evolving to cover these emerging exposures, offering comprehensive protection for vendors operating in diverse deployment scenarios. This trend is expected to accelerate as enterprises continue to embrace cloud-native architectures and distributed data ecosystems.

From a regional perspective, North America currently dominates the Vector Database Vendor Liability Insurance market, accounting for over 41% of global revenue in 2024. This leadership position is attributed to the region’s advanced technology landscape, high concentration of database vendors, and proactive regulatory frameworks. However, Asia Pacific is poised for the fastest growth over the forecast period, with a projected CAGR of 17.2%, driven by rapid digital transformation, expanding cloud adoption, and increasing awareness of cyber risks among enterprises. Europe remains a significant market, supported by robust data protection regulations and a strong ecosystem of technology providers. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, albeit from a smaller base, as organizations in these regions ramp up investments in digital infrastructure and risk management solutions.

Vector Database Vendor Liability Insurance Market Outlook

According to our latest research, the global Vector Database Vendor Liability Insurance market size reached USD 1.42 billion in 2024, reflecting the increasing need for specialized insurance products tailored to the rapidly growing vector database industry. The market is projected to expand at a robust CAGR of 17.5% from 2025 to 2033, reaching an estimated USD 6.05 billion by 2033. This remarkable growth is primarily driven by heightened concerns around data security, regulatory compliance, and the unique risk landscape facing vector database vendors. As organizations globally accelerate their adoption of artificial intelligence, machine learning, and advanced analytics platforms, the demand for comprehensive liability coverage is becoming a critical component of enterprise risk management strategies.

One of the primary growth factors propelling the Vector Database Vendor Liability Insurance market is the increasing complexity and sophistication of cyber threats targeting data-centric platforms. Vector databases, which are essential for powering AI-driven applications, are particularly vulnerable to data breaches, unauthorized access, and operational disruptions. As a result, vendors are seeking insurance solutions that specifically address these emerging risks, including coverage for cyber liability, professional errors, and product-related incidents. The proliferation of high-value data assets, coupled with the rising frequency of high-profile cyberattacks, is compelling companies to invest in robust insurance policies that can mitigate potential financial losses, legal liabilities, and reputational damage.

Another significant driver of market growth is the evolving regulatory landscape governing data privacy and security across different regions. Jurisdictions such as the European Union, the United States, and parts of Asia Pacific have introduced stringent compliance requirements, including GDPR, CCPA, and other data protection frameworks. These regulations mandate that organizations demonstrate accountability for data handling, storage, and processing, thereby increasing the demand for liability insurance among vector database vendors. Insurance providers are responding to this trend by offering tailored coverage that addresses regulatory fines, legal defense costs, and remediation expenses, further fueling market expansion. Additionally, as more enterprises migrate their operations to cloud-based environments, the need for insurance products that cover both on-premises and cloud-native risks is gaining prominence.

The market is also experiencing robust growth due to the increasing adoption of vector databases by enterprises of all sizes, from startups to large multinational corporations. As organizations integrate vector databases into their core business operations to enhance AI capabilities, the associated risks and liabilities become more complex and multifaceted. This has led to a surge in demand for specialized insurance products that can provide comprehensive coverage for a wide range of scenarios, including professional errors, product failures, and third-party claims. Insurance providers are innovating their offerings to keep pace with the evolving risk landscape, introducing new policy features and flexible coverage options that cater to the unique needs of vector database vendors. This trend is expected to continue over the forecast period, driving sustained growth in the Vector Database Vendor Liability Insurance market.

From a regional perspective, North America currently dominates the Vector Database Vendor Liability Insurance market, accounting for the largest share due to the high concentration of technology companies, advanced regulatory frameworks, and a mature insurance ecosystem. Europe follows closely, driven by strong data protection regulations and the widespread adoption of AI technologies across various industries. The Asia Pacific region is emerging as a significant growth market, fueled by rapid digital transformation, increasing investments in AI infrastructure, and rising awareness of data security risks. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions begin to recognize the importance of liability insurance in managing technology-related risks. Overall, the global market is characterized by strong demand across all major regions, with each exhibiting unique growth drivers and challenges.

Clause Library Management for Insurers Market Outlook

According to our latest research, the global Clause Library Management for Insurers market size reached USD 1.32 billion in 2024, reflecting a robust upward trajectory driven by digital transformation across the insurance industry. The market is anticipated to expand at a CAGR of 12.7% from 2025 to 2033, with the total market size projected to reach USD 3.88 billion by 2033. This growth is primarily fueled by increasing regulatory requirements, the need for operational efficiency, and the adoption of advanced technologies such as artificial intelligence and automation within insurance operations.

The primary growth factor for the Clause Library Management for Insurers market is the escalating complexity of insurance products and the corresponding need for standardized, compliant, and rapidly adaptable policy documentation. Insurers are under constant pressure to innovate their offerings while ensuring that all policy clauses are up-to-date with the latest regulations and market practices. This has made clause library management systems indispensable, as they enable insurers to maintain a centralized repository of pre-approved clauses, streamline policy creation, and significantly reduce the risk of non-compliance. Furthermore, as insurers expand into new markets with diverse regulatory environments, the necessity for robust clause management tools becomes even more apparent, driving market demand.

Another significant driver is the digital transformation sweeping through the insurance sector. Insurers are increasingly investing in technology to automate and optimize their core processes, including policy and claims management. Clause library management solutions are integral to this transformation, as they facilitate faster policy issuance, improve accuracy, and enhance customer experience by enabling more personalized and flexible insurance products. The integration of these solutions with other core insurance systems, such as underwriting and claims platforms, is further amplifying their value proposition. As a result, both established insurers and insurtech startups are prioritizing investments in clause library management to remain competitive and agile in a rapidly evolving market landscape.

Moreover, the growing focus on risk management and compliance is compelling insurers to adopt advanced clause library solutions. Regulatory bodies worldwide are imposing stricter requirements on insurance documentation, making it imperative for insurers to ensure that every clause in their contracts is compliant and up-to-date. Clause library management tools not only facilitate compliance but also provide audit trails and reporting capabilities that are essential for regulatory audits. This trend is particularly pronounced in regions with dynamic regulatory frameworks, such as North America and Europe, where insurers face heightened scrutiny and frequent regulatory changes. Consequently, the demand for sophisticated clause library management systems is expected to remain strong, driving sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the Clause Library Management for Insurers market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced insurance technologies, coupled with stringent regulatory requirements, has positioned North America as the leading market. However, Asia Pacific is anticipated to exhibit the fastest growth rate over the forecast period, driven by the rapid digitalization of the insurance industry, expanding insurance penetration, and increasing regulatory focus on compliance and risk management. Latin America and the Middle East & Africa are also expected to witness steady growth, albeit from a smaller base, as insurers in these regions gradually embrace digital transformation and modernize their operations.

Component Analysis

The Clause Library Management for Insurers market is segmented by component into software and services. The software segment currently holds the dominant share, reflecting the widespread adoption of advanced clause management platforms that offer robust features such as template management, clause versioning, and integration with core insurance systems. These software solutions are designed to automate and streamline the process of managing policy clauses, ensuring consistency, compliance, and efficiency

CompanyData.com (BoldData) provides accurate, verified business intelligence sourced directly from official trade registers and financial authorities. Our global database includes 1 million banking and insurance companies, giving you unrivaled access to financial institutions, commercial banks, fintech firms, life insurers, reinsurers, and investment companies across every major market.

Each record in our database is enriched with high-value details such as company hierarchies, executive contacts, email addresses, direct phone numbers, mobile numbers, industry codes, and firmographic data including company size, revenue, and location. This ensures you get not just quantity, but precision and relevance for your business needs. Our data is continually verified and updated to meet the strictest accuracy and compliance standards.

Organizations worldwide use our financial services dataset for a wide range of applications—from regulatory compliance and KYC verification, to financial services sales outreach, marketing campaigns, CRM or ERP database enrichment, and AI training models. Whether you're targeting insurance providers in Europe or identifying investment firms in Asia, our dataset provides the clarity and coverage to move forward with confidence.

You can access the data through custom-tailored bulk downloads, real-time API integrations, or explore and filter companies directly through our easy-to-use self-service platform. With a total coverage of 380 million verified companies globally, CompanyData.com (BoldData) is your trusted partner for navigating the complex and regulated landscape of global finance and insurance.

Large proprietary database and extensive network to identify insurance candidates nationally

United States Property & Casualty Insurance: Net Underwriting Gain (Loss ) data was reported at 24.989 USD bn in Dec 2024. This records an increase from the previous number of 6.184 USD bn for Sep 2024. United States Property & Casualty Insurance: Net Underwriting Gain (Loss ) data is updated quarterly, averaging 4.567 USD bn from Mar 2012 (Median) to Dec 2024, with 52 observations. The data reached an all-time high of 24.989 USD bn in Dec 2024 and a record low of -30.037 USD bn in Sep 2023. United States Property & Casualty Insurance: Net Underwriting Gain (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.RG012: Property & Casualty Insurance: Industry Financial Snapshots.

Extensive database covering all levels and disciplines within commercial P&C insurance

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).The file is georeferenced to earth's surface using the Lambert Conformal Conic projection and the Louisiana State Plane NAD83 South Zone coordinate system. The specifications for the horizontal control of Base Map data files are consistent with those required for mapping at a scale of 1:24,000

Context

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

Content

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

Acknowledgements

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

Inspiration

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

Context

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

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

Content

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

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

Acknowledgements

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

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

Inspiration

Hurricane season is always right around the corner.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).The file is georeferenced to earth's surface using the Lambert Conformal Conic projection and the Arkansas State Plane NAD83 South Zone coordinate system. The specifications for the horizontal control of Base Map data files are consistent with those required for mapping at a scale of 1:24,000

Insurance Statistics: No of Registered Insurers: Mandatory Insurance data was reported at 2.000 Unit in 2023. This records a decrease from the previous number of 3.000 Unit for 2022. Insurance Statistics: No of Registered Insurers: Mandatory Insurance data is updated yearly, averaging 3.000 Unit from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 3.000 Unit in 2022 and a record low of 2.000 Unit in 2023. Insurance Statistics: No of Registered Insurers: Mandatory Insurance data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA001: Insurance Statistics: Key Indicators.

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

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

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

This layer is a component of Region Preliminary Data.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the State Plane projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

Normally, any FIRM that has associated flood profiles has cross sections. The S_XS table contains information about cross section lines. These lines usually represent the locations of channel surveys performed for input into the hydraulic model used to calculate flood elevations. Sometimes cross sections are interpolated between surveyed cross sections using high accuracy elevation data. Depending on the zone designation (Zone AE, Zone A, etc.), these locations may be shown on Flood Profiles in the FIS report and can be used to cross reference the Flood Profiles to the planimetric depiction of the flood hazards. This information is used in the Floodway Data Tables in the FIS report, as well as on the FIRM panels.

This layer is a component of Region Preliminary Data.

description: The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.; abstract: The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk; classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).The file is georeferenced to earth's surface using the Lambert Conformal Conic projection and the Arkansas State Plane NAD83 South Zone coordinate system. The specifications for the horizontal control of Base Map data files are consistent with those required for mapping at a scale of 1:24,000