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.

Description: This dataset contains 1,000 rows of synthetic data simulating car insurance premiums, calculated using a linear formula. It incorporates key features such as driver age, driving experience, accident history, annual mileage, and car manufacturing year to predict the insurance premium. The dataset is ideal for exploring linear regression models, feature importance analysis, and predictive modeling in the insurance industry. It was inspired by real-world factors influencing insurance premiums, ensuring realistic patterns and meaningful insights.

The provided data asset is relational and consists of four distinct data files.

1. address.csv: contains address information

2. customer.csv: contains customer information.

3. demographic.csv: contains demographic data

4. termination.csv: includes customer termination information.

5. autoinsurance_churn.csv: includes merged customer churn data generated from this notebook.

All data sets are linked using either ADDRESS_ID or INDIVIDUAL_ID. The ADDRESS_ID pertains to a specific postal service address, while the INDIVIDUAL_ID is unique to each individual. It is important to note that multiple customers may be assigned to the same address, and not all customers have demographic information available.

Size of the data set

The data set includes 1,536,673 unique addresses and 2,280,321 unique customers, of which 2,112,579 have demographic information. Additionally, 269,259 customers cancelled their policies within the previous year.

Note

Please note that the data is synthetic, and all customer information provided is fictitious. While the latitude-longitude information can be mapped at a high level and generally refers to the Dallas-Fort Worth Metroplex in North Texas, it is important to note that drilling down too far may result in some data points that are located in the middle of Jerry World, DFW Airport, or Lake Grapevine. The physical addresses provided are fake and are unrelated to the corresponding lat/long.

The termination table includes the ACCT_SUSPD_DATE field, which can be used to derive a binary churn/did not churn variable. The data set is modelable, meaning that the other data available can be used to predict which customers churned and which did not. The underlying logic used to make these predictions should align with predicting auto insurance churn in the real world.

This dataset is a synthetic yet realistic representation of personal auto insurance data, crafted using real-world statistics. While actual insurance data is sensitive and unavailable for public use, this dataset bridges the gap by offering a safe and practical alternative for building robust data science projects.

Why This Dataset? - Realistic Foundation: Synthetic data generated from real-world statistical patterns ensures practical relevance. - Safe for Use: No personal or sensitive information—completely anonymized and compliant with data privacy standards. - Flexible Applications: Ideal for testing models, developing prototypes, and showcasing portfolio projects.

How You Can Use It: - Build machine learning models for predicting customer conversion and retention. - Design risk assessment tools or premium optimization algorithms. - Create dashboards to visualize trends in customer segmentation and policy data. - Explore innovative solutions for the insurance industry using a realistic data foundation.

This dataset empowers you to work on real-world insurance scenarios without compromising on data sensitivity.

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.

Data provided by insurers, on the premiums written and claims incurred for the 2013 fiscal year. Based on reporting on the consolidated pages of the P&C-1 or Life-1 Annual returns. This data is also reported in the Superintendent of Insurance’s Annual Report.

Louisiana had the most expensive annual car insurance premiums at ***** U.S. dollars for full coverage. Alaska ranked in first place, having the highest annual cost for minimum car insurance coverage at *** U.S. dollars.Why it varies state by state The huge variance in premiums between states is due to the difference in state laws, the percentage of uninsured drivers in the state, the frequency of natural disasters, and claim rates. For instance, Michigan has a no-fault car insurance system, which means that claims are more common. This drives up the cost of insurance for all drivers because insurers need to pay out more money in claims. Male drivers also pay more There is also a difference between premiums among different age groups. In 2025, 25-year-old male drivers paid more per month than 25-year-old female drivers did. This is due to the higher incidence of accidents among young male drivers. This means that young drivers in states that already have higher premiums must pay a lot for car insurance.

Vehicle Insurance Fraud Detection

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

About this dataset

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

Insurance Quote in Car with Data Consent Market Outlook

According to our latest research, the global Insurance Quote in Car with Data Consent market size reached USD 3.1 billion in 2024, reflecting a robust momentum driven by digital transformation and increasing consumer acceptance of data sharing in automotive insurance. The market is exhibiting a strong growth trajectory, with a CAGR of 17.2% projected for the period from 2025 to 2033. By 2033, the Insurance Quote in Car with Data Consent market is forecasted to achieve a value of USD 13.6 billion. This growth is primarily attributed to the proliferation of connected vehicles, advancements in telematics technology, and the evolving regulatory landscape that encourages responsible data usage within the insurance sector.

The expansion of the Insurance Quote in Car with Data Consent market is underpinned by several compelling growth factors. One of the most significant drivers is the rapid integration of telematics and IoT devices in modern vehicles, allowing insurers to collect real-time data on driving behavior, vehicle health, and usage patterns. This granular data enables insurers to offer highly personalized insurance quotes, fostering greater transparency and trust between providers and policyholders. Furthermore, consumers are increasingly recognizing the benefits of sharing data, such as lower premiums and customized coverage, which incentivizes participation and propels market adoption. The convergence of automotive and insurance technologies has also led to the emergence of innovative business models, such as usage-based insurance (UBI), further fueling market growth.

Another pivotal growth factor is the evolving regulatory environment that governs data privacy and consent. Governments and regulatory bodies across major regions are enacting stringent data protection laws, such as GDPR in Europe and CCPA in California, which mandate explicit consumer consent for data collection and usage. While these regulations pose compliance challenges, they also instill confidence among consumers regarding the security and ethical use of their personal and vehicular data. This regulatory clarity is encouraging more automotive OEMs and insurance companies to invest in advanced consent management solutions, thereby expanding the addressable market. Additionally, the rise of digital platforms and mobile applications has simplified the consent process, making it easier for users to understand and manage their data preferences.

The proliferation of connected vehicles and advancements in artificial intelligence (AI) and machine learning (ML) are further accelerating the market’s evolution. Insurers are leveraging sophisticated algorithms to analyze vast datasets collected from vehicles, enabling them to assess risk more accurately and tailor products to individual needs. This data-driven approach not only enhances underwriting precision but also streamlines claims processing and fraud detection. As a result, insurance companies are able to improve operational efficiency and customer satisfaction, which in turn drives higher retention rates and market expansion. The integration of AI-powered analytics and real-time data processing is expected to remain a key differentiator for leading market players in the coming years.

From a regional perspective, North America currently dominates the Insurance Quote in Car with Data Consent market, owing to its mature automotive industry, widespread adoption of telematics, and favorable regulatory framework. Europe follows closely, driven by strong regulatory mandates around data consent and a high penetration of connected vehicles. The Asia Pacific region is poised for the fastest growth, supported by rapid urbanization, increasing vehicle ownership, and rising awareness of data-driven insurance solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, as infrastructure and regulatory frameworks continue to evolve. Overall, the global market is characterized by dynamic regional trends that reflect varying levels of technological adoption, regulatory maturity, and consumer readiness.

Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.

Connected Insurance Data Exchange Market Outlook

According to our latest research, the global Connected Insurance Data Exchange market size reached USD 3.8 billion in 2024 and is projected to grow at a CAGR of 20.7% during the forecast period, reaching approximately USD 24.1 billion by 2033. This robust growth is driven by the increasing adoption of digital technologies in the insurance sector, the proliferation of IoT and telematics devices, and the rising demand for personalized insurance products and real-time risk assessment. As per our comprehensive analysis, the market continues to evolve rapidly, propelled by innovation and the integration of advanced data exchange platforms within traditional insurance operations.

One of the primary growth factors for the Connected Insurance Data Exchange market is the exponential rise in connected devices, such as telematics, IoT sensors, wearables, and smart home systems. These devices generate vast volumes of real-time data, enabling insurers to better assess risk, offer dynamic pricing, and personalize insurance products. The surge in telematics adoption in automotive insurance, for instance, allows insurers to monitor driving behavior and vehicle health, resulting in more accurate underwriting and claims management. Similarly, wearables in health insurance provide continuous health monitoring data, supporting proactive wellness programs and reducing claim costs. This data-driven approach is revolutionizing the insurance industry, fostering greater transparency, efficiency, and customer engagement.

Another significant driver is the growing demand for seamless and integrated digital experiences among policyholders. Modern consumers expect insurance services to be as agile and responsive as other digital services they use daily. The Connected Insurance Data Exchange platforms enable insurers to leverage real-time data streams, automate claims processing, and provide instant policy updates, all of which enhance customer satisfaction and retention. Furthermore, regulatory initiatives in regions such as North America and Europe are encouraging data sharing and interoperability, further accelerating the adoption of these platforms. The ability to aggregate data from multiple sources and deliver actionable insights is becoming a critical differentiator for insurers seeking to remain competitive in an increasingly digital landscape.

Technological advancements in artificial intelligence, machine learning, and big data analytics also play a pivotal role in shaping the Connected Insurance Data Exchange market. These technologies empower insurers to extract deeper insights from vast datasets, enabling predictive analytics, fraud detection, and automated decision-making. As insurance companies invest in modernizing their IT infrastructure and integrating advanced analytics into their workflows, the demand for robust data exchange platforms continues to surge. Moreover, partnerships between insurers, technology providers, and third-party administrators are fostering an ecosystem of innovation, further expanding the marketÂ’s potential. The convergence of these factors is expected to sustain high growth rates over the next decade.

The concept of Insurance Data-as-a-Service is gaining traction as insurers seek to leverage vast amounts of data to enhance their offerings. This service model allows insurance companies to access and utilize data from various sources, such as telematics, IoT devices, and wearables, without the need to manage the underlying infrastructure. By adopting Insurance Data-as-a-Service, insurers can focus on analyzing data to gain insights into customer behavior, risk patterns, and market trends. This approach not only reduces operational costs but also accelerates the development of innovative insurance products tailored to individual needs. As the demand for real-time data and personalized services continues to grow, Insurance Data-as-a-Service is poised to become a key enabler of digital transformation in the insurance industry.

From a regional perspective, North America currently leads the Connected Insurance Data Exchange market, driven by early adoption of digital insurance solutions, a mature insurance ecosystem, and supportive regulatory frameworks. Europe follows closely, with significant investments in connected car and health insurance segm

OVERVIEW In March 2019, Poverty Solutions released “AUTO INSURANCE AND ECONOMIC MOBILITY IN MICHIGAN: A CYCLE OF POVERTY”, a policy brief detailing the sources of Michigan’s highest-in-the-nation auto insurance rates and providing policy options for policymaker seeking to enact changes that would reduce overall rates and reduce rate disparities. The report pulled data from The Zebra, an auto insurance comparison marketplace, to show the distribution of rates by ZIP code and to calculate a cost burden for each ZIP code. DATAThe Zebra – provides ZIP code level data on average auto insurance rates from 2011-2017. The data represents an average of market prices facing a consistent base consumer profile. According to the Zebra, “Analysis used a consistent base profile for the insured driver: a 30-year-old single male driving a 2014 Honda Accord EX with a good driving history and coverage limits of $50,000 bodily injury liability per person/$100,000 bodily injury liability per accident/$50,000 property damage liability per accident with a $500 deductible for comprehensive and collision”.[1] For more information on The Zebra’s data collection methodology go to www.thezebra.com.Click here for metadata (descriptions of the fields).

In 2022, there were more than ************* auto insurance claims submitted in Germany. The largest share was for comprehensive, or Vollkasko, insurance, which accounted for *** million claims, followed by third-party liability with **** million claims.

ABSTRACT Context: triggered in 2014, the Car Wash Operation (CWO) belongs to a process of changing the legal context, in the sense of greater responsibility and penalization of public and private companies’ decision makers for acts practiced in the exercise of their functions, object of the Directors’ and Officers’ liability insurance coverage (D&O). Objective: to evaluate the relationship between the growth in the revenues of D&O insurance premiums and the developments of the OCW in Brazil, under the hypothesis of a change in the perception of economic agents exposed to risks covered by D&O insurance, in a process known as probability updating. Methods: official monthly data for all active insurers, arranged longitudinally between 2003 and 2017, and using two-stage regression method for panel data. Results: the OCW had a positive effect not only to the probability of offering this type of insurance, but also to increase the volume of D&O premiums; these results are consistent with the probability-updating hypothesis. Conclusion: the OCW resulted in an increase in revenues of D&O premiums, but there was a negative relationship between OCW and the entire insurance market, suggesting significance of this operation in the sector retraction observed since its outbreak.

The auto-insurance industry is witness paradigm shift. since auto insurance company consist of homogeneous good thereby making it difficult to differentiate product A from product B ,also companies fight for price war,on top of that distribution channel is shifting more from traditional insurance brokers to online purchase which means ability for companies to interact through human touch points is limited and customers should be quoted at a reasonable price .A good price quote make customer to purchase policy and make company to increase profits .Also insurance premium is calculated based on more than 50+ parameters which mans that traditional business analytics based are now limited in their ability to differentiate among customers based on subtle parameter

1.Conquering Market Share 2.Risk Management 3.Increase Profit

The project involve use of dataset with 600k training data and 57 features .In train and test data ,features that are belong to similar group are tagged in feature name ex:(ind,reg,calc,car).In addition feature name include postfix bin to indicate binary feature and cat to categorical feature .feature without these designation are either continous or ordinal

Acknowledgements

EDUREKA

AI-Driven Insurance Underwriting Market Outlook

According to our latest research, the AI-Driven Insurance Underwriting market size reached USD 4.2 billion globally in 2024, reflecting robust adoption across insurance verticals. The market is expanding at a notable CAGR of 22.8% and is forecasted to attain a value of USD 32.1 billion by 2033. This remarkable growth trajectory is primarily fueled by the increasing demand for automation, enhanced risk assessment capabilities, and the need for faster, more accurate underwriting decisions in the insurance industry.

The surge in the AI-Driven Insurance Underwriting market is largely attributed to the mounting pressure on insurers to streamline operations and reduce costs while improving the accuracy of risk evaluation. As customer expectations evolve, insurers are leveraging advanced AI technologies to automate and optimize underwriting processes, thus reducing manual errors and expediting policy issuance. The integration of machine learning algorithms and predictive analytics enables insurers to analyze vast datasets, identify patterns, and make data-driven decisions with greater precision. This shift toward digital transformation not only enhances operational efficiency but also bolsters customer satisfaction by delivering quicker and more personalized insurance solutions.

Another significant growth factor is the increasing availability and sophistication of big data and analytics tools. Insurers now have access to diverse data sources, including social media, telematics, and IoT devices, which provide granular insights into customer behavior and risk profiles. AI-powered underwriting platforms can process and synthesize these large datasets in real time, enabling insurers to develop more accurate pricing models and reduce instances of fraud. Moreover, regulatory compliance is driving insurers to adopt transparent and explainable AI models, ensuring that automated decisions are fair, unbiased, and auditable, thereby building trust among stakeholders and regulatory authorities.

The proliferation of cloud computing and advancements in AI infrastructure are further accelerating the adoption of AI-driven underwriting solutions. Cloud-based platforms offer scalability, flexibility, and cost-efficiency, allowing insurers of all sizes to deploy sophisticated underwriting systems without significant upfront investments in hardware. Additionally, the rise of InsurTech startups and strategic partnerships with technology vendors are fostering innovation in the market, leading to the development of customized AI solutions tailored to specific insurance segments such as life, health, property, and auto insurance. These collaborative efforts are expanding the reach of AI-driven underwriting and driving market growth across both developed and emerging economies.

AI-Powered Cyber Insurance is becoming an increasingly vital component in the insurance industry, especially as cyber threats continue to evolve and become more sophisticated. Insurers are leveraging AI technologies to assess cyber risks more accurately, offering tailored policies that address specific vulnerabilities in an organization's digital infrastructure. By analyzing patterns in cyber incidents and predicting potential threats, AI-powered solutions enable insurers to provide more comprehensive coverage and proactive risk management strategies. This not only helps in mitigating potential losses but also enhances customer trust by ensuring robust protection against emerging cyber risks. As businesses continue to digitize their operations, the demand for AI-Powered Cyber Insurance is expected to grow, driving innovation and collaboration between insurers and technology providers.

Regionally, North America remains at the forefront of the AI-Driven Insurance Underwriting market, owing to its mature insurance sector, high digital adoption rates, and strong presence of leading technology providers. However, the Asia Pacific region is witnessing the fastest growth, propelled by rapid economic development, rising insurance penetration, and increasing investments in digital infrastructure. Europe is also making significant strides, particularly in markets like the UK, Germany, and France, where regulatory support and innovation-friendly environments are encouraging insurers to embrace AI

Here are a few use cases for this project:

1. Auto Insurance Claims: The “CARS” model could be used by insurance companies to automate the process of verifying and categorizing car damage during the claims process. By evaluating images of the cars, accuracy of identifying dents and scratches can lead to more efficient and swift claim processing.

2. Car Dealerships or Rental Services: This model can be used in car dealerships or car rental services to identify existing damage on a vehicle before it is sold or rented out. It can help document the condition of the car to avoid future disputes about the origin of the damage.

3. Car Repair Services: In automotive repair shops, the model can be used to initially examine and document the extent of a car's damage. This can help mechanics make more accurate estimations for repairs and help customers understand the work that needs to be done.

4. Used Car Platforms: Online platforms selling used cars can implement the "CARS" model to ensure that seller descriptions of car condition are accurate. This ensures transparency and adds credibility to listed goods.

5. Fleet Management: For companies managing large fleets of vehicles, the model can be used for regular inspections, catching minor damages like dents and scratches early before they develop into bigger, more expensive problems. It can streamline the maintenance process and improve upkeep of the fleet.

Stock Price Time Series for Goosehead Insurance Inc. Goosehead Insurance, Inc operates as a holding company for Goosehead Financial, LLC that engages in the provision of personal lines insurance agency services in the United States. The company offers insurance service for homeowner's; automotive; dwelling property; flood, wind, and earthquake; excess liability or umbrella; general liability, and property and auto insurance for small businesses; life insurance; and motorcycle, recreational vehicle, and other insurance policies. It distributes its products and services through corporate and franchise locations. The company was founded in 2003 and is headquartered in Westlake, Texas.

Insurance Pricing Optimization Market Outlook

According to our latest research, the global insurance pricing optimization market size reached USD 1.95 billion in 2024, and is anticipated to expand at a robust CAGR of 13.2% during the forecast period, reaching an estimated USD 5.18 billion by 2033. The market’s rapid growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence to enhance pricing accuracy, profitability, and customer segmentation in the insurance sector.

One of the most significant growth factors fueling the insurance pricing optimization market is the rising complexity and competitiveness within the insurance industry. Insurers are under constant pressure to balance profitability with customer retention, and traditional pricing models often fall short in this dynamic environment. Advanced pricing optimization solutions allow insurers to leverage big data, AI, and machine learning algorithms to analyze vast datasets, identify risk patterns, and tailor prices at an individual level. This not only ensures more precise risk assessment but also enhances the insurer’s ability to offer competitive premiums, thereby improving acquisition and retention rates. The integration of these technologies into core insurance processes is transforming the way insurers approach pricing, making it a central pillar of business strategy.

Another crucial growth driver is the increasing regulatory scrutiny and evolving compliance requirements across global markets. Regulatory bodies are demanding greater transparency and fairness in pricing, especially in auto and health insurance segments. Pricing optimization solutions empower insurers to demonstrate compliance by providing clear audit trails and rationales for pricing decisions. These solutions also support the implementation of fair pricing practices, reducing the risk of regulatory penalties and reputational damage. Additionally, the growing focus on personalized insurance products and usage-based models is pushing insurers to adopt sophisticated pricing tools that can dynamically adjust premiums based on real-time data, further accelerating market expansion.

The surge in digital transformation initiatives across the insurance industry is also playing a pivotal role in market growth. Insurers are investing heavily in digital platforms, cloud computing, and advanced analytics to streamline operations and enhance customer experience. Pricing optimization is at the heart of this transformation, enabling insurers to respond swiftly to market changes, customer preferences, and emerging risks. The proliferation of InsurTech startups and partnerships with technology providers is fostering innovation and expanding the availability of cutting-edge pricing solutions. Furthermore, the increasing adoption of telematics, IoT devices, and mobile platforms is generating vast amounts of data, which can be harnessed by pricing optimization tools to deliver more accurate and dynamic pricing strategies.

Regionally, North America continues to dominate the insurance pricing optimization market, owing to the early adoption of advanced analytics, a mature insurance sector, and the presence of leading technology providers. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding insurance penetration, and increasing investments in AI and big data analytics. Europe is also witnessing significant growth, supported by stringent regulatory frameworks and a strong focus on customer-centric insurance models. Latin America and the Middle East & Africa are gradually catching up, as insurers in these regions seek to modernize their pricing strategies and improve operational efficiency.

Component Analysis

The insurance pricing optimization market, when segmented by component, is primarily divided into software and services. The software segment currently holds the largest market share, driven by the widespread adoption of advanced analytics, machine learning, and artificial intelligence platforms that empower insurers to automate and refine their pricing strategies. These software solutions are designed to handle vast datasets, integrate seamlessly with existing core insurance systems, and provide real-time insights into pricing effectiveness. As insurers increasingly prioritize digital transformation, demand for robust and scalable software platforms continues to surge, enabling them to stay competitive in a rapidly evolving landscape.

Autobi(Automobile Bodily Injury Claims) -

The data contains information on demographic information about the claimant, attorney involvement and the economic loss (LOSS, in thousands), among other variables.The full data contains over 70,000 closed claims based on data from thirty-two insurers.

A data frame with 1340 observations on the following 8 variables.

CASENUM- Case number to identify the claim, a numeric vector ATTORNEY- Whether the claimant is represented by an attorney (=1 if yes and =2 if no), a numeric vector CLMSEX - Claimant's gender (=1 if male and =2 if female), a numeric vector MARITAL- claimant's marital status (=1 if married, =2 if single, =3 if widowed, and =4 if divorced/separated), a numeric vector CLMINSUR- Whether or not the driver of the claimant's vehicle was uninsured (=1 if yes, =2 if no, and =3 if not applicable), a numeric vector SEATBELT- Whether or not the claimant was wearing a seatbelt/child restraint (=1 if yes, =2 if no, and =3 if not applicable), a numeric vector CLMAGE- Claimant's age, a numeric vector LOSS- The claimant's total economic loss (in thousands), a numeric vector

AutoClaims(Automobile Insurance Claims) -

A data frame with 6773 observations on the following 5 variables.

STATE CLASS - Rating class of operator, based on age, gender, marital status, use of vehicle GENDER AGE - Age of operator PAID - Amount paid to settle and close a claim

AutoCollision(Automobile UK Collision Claims)

8,942 collision losses from private passenger United Kingdom (UK) automobile insurance policies. The average severity is in pounds sterling adjusted for inflation.

A data frame with 32 observations on the following 4 variables.

Age - Age of driver Vehicle_Use - Purpose of the vehicle use Severity - Average amount of claims Claim_Count - Number of claims

Additional information can be found in the document: https://cran.r-project.org/web/packages/insuranceData/index.html