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.

State Farm Mutual Automobile Insurance was the leading private passenger car insurer in the United States in 2024, with premiums written amounting to approximately 68 billion U.S. dollars. Progressive Corporation, and Berkshire Hathaway Inc. were the next largest insurers in this sector. State Farm: a background State Farm Mutual Automobile Insurance was founded in 1922 and is headquartered in Bloomington, Illinois. In 2024, the insurer was the largest writer of property and casualty insurance in the United States. They provide vehicle, homeowners, renters, life and annuities, health, disability and flood insurance among several other insurance products. Net promoter score and ad spend of State Farm Despite their market leader status, State Farm's net promoter score puts them in the middle of the pack, with only 42 percent of their customers saying they would recommend the insurer. However, their nearest competitors did not score any better, with Progressive receiving a NPS of only 38 percent in the same analysis. The three largest car insurers were also the biggest spenders on advertising.

The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (November 2015 to December 2018) concerning non-life motor insurance portfolio. This dataset comprises 105,555 rows and 30 columns. Each row signifies a policy transaction, while each column represents a distinct var

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.

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.

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

Graph and download economic data for Producer Price Index by Industry: Direct Property and Casualty Insurers: Private Passenger Auto Insurance (PCU5241265241261) from Jun 1998 to Sep 2025 about property-casualty, passenger, insurance, vehicles, private, PPI, industry, inflation, price index, indexes, price, and USA.

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.

Graph and download economic data for Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Commercial Auto Insurance (PCU9241269241263) from Jun 1998 to Aug 2025 about property-casualty, premium, insurance, vehicles, commercial, PPI, industry, inflation, price index, indexes, price, and USA.

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

This data set is used for actuarial and financial application regression modeling case studiesThese data are provided by the Swedish non life insurance commission and include the data of auto insurance claims in 2010.The result of interest is the number (frequency) of claims and the total amount of payments (severity), in SEK. Results based on the driving distance of 5 types of vehicles, it is subdivided according to 7 geographical regions, 7 types of recent driver claim experience and 9 types of vehicles.

16-year-olds paid the highest average monthly amount for car insurance in the United States. It was found that 16-year-old drivers in the U.S. had to pay approximately *** U.S. dollars per month for car insurance, whereas their 21-year-old counterparts paid *** U.S. dollars for the same coverage.

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Motor Vehicle Insurance Market Size 2024-2028

The motor vehicle insurance market size is forecast to increase by USD 545.9 billion, at a CAGR of 10.44% between 2023 and 2028.

The market is experiencing significant shifts driven by increasing government regulations on mandatory insurance coverage in developing countries and the digitalization of the industry. These factors are shaping the market's strategic landscape, presenting both opportunities and challenges for insurance players. Government regulations in developing countries are pushing for mandatory insurance coverage, expanding the potential customer base for motor vehicle insurers. This trend is particularly noticeable in Asia Pacific and Latin America, where economic growth and urbanization are leading to increased car ownership. However, this regulatory environment also tightens the competitive landscape, as more players enter the market and compliance becomes a priority.
Simultaneously, the digitalization of the motor vehicle insurance industry is transforming the way insurers engage with customers and manage risk. Digital platforms enable real-time underwriting, claims processing, and customer service, enhancing the overall customer experience. However, this digital shift also brings challenges, such as data security concerns and the need for robust IT infrastructure. To capitalize on opportunities and navigate challenges effectively, insurers must stay abreast of regulatory changes and invest in digital capabilities.

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

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, shaped by dynamic market forces and advancements in technology. AI-powered claims processing streamlines underwriting and settlement negotiations, while digital insurance platforms offer convenience and personalized pricing. Data analytics and credit scoring inform risk assessment and customer segmentation, shaping insurance regulations and product offerings. Collision coverage and liability limits are subject to ongoing adjustments, influenced by factors such as driving record and insurable interest. Third-party administrators (TPAs) and legal counsel facilitate dispute resolution, ensuring regulatory compliance and comparative negligence assessments. Fraud detection and independent verification are essential components of claims processing, with advanced predictive modeling and accident reconstruction techniques aiding in claims investigation and policy administration.

How is this Motor Vehicle Insurance Industry segmented?

The motor vehicle insurance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Personal
  Commercial


Distribution Channel

  Brokers
  Direct
  Banks
  Others


Vehicle Age

  New Vehicles
  Old Vehicles
  New Vehicles
  Old Vehicles


Coverage Type

  Liability Insurance
  Collision Insurance
  Comprehensive Insurance


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The personal segment is estimated to witness significant growth during the forecast period.

Motor vehicle insurance is a crucial financial protection for vehicle owners and drivers. The insurance policy, which is a compulsory requirement under the Motor Policy, offers coverage for both comprehensive and third-party liability packages. Personal insurance, an optional add-on cover, safeguards the owner or driver against accidental injuries. Insurance agents and brokers play a significant role in advising clients on coverage limits and policy options. Actuarial modeling and predictive analytics are used to assess risk and determine personalized pricing. Liability coverage, including property damage and bodily injury, is a key component of motor vehicle insurance. Fraud detection and independent verification are essential for dispute resolution and maintaining regulatory compliance.

Digital insurance platforms and ai-powered claims processing streamline the claims management process. Data analytics and customer segmentation help insurers tailor policies to individual needs. Usage-based insurance and mobile apps provide real-time data for risk assessment and customer retention. Insurance regulations mandate coverage for medical payments and accident reconstruction, as well as policy administration and claims processing. Policy cancellation, clai

This dataset comprises 9,134 records of auto insurance claims, encompassing a broad range of attributes related to customer profiles and policy details. Key columns include demographic information such as Customer, State, Gender, Income, and Education, along with policy-specific data like Coverage, Policy Type, and Monthly Premium Auto. This dataset also contains indices for various categorical attributes, including Coverage Index, Education Index, and Vehicle Class Index, which facilitate the quantification of qualitative information. Additionally, the dataset tracks metrics related to policy performance and customer interaction, such as the Number of Open Complaints, Months Since Last Claim, and Total Claim Amount.

To provide a comprehensive view of the insurance landscape, the dataset includes detailed attributes about policy effectiveness and customer engagement. Features such as Effective To Date, Renew Offer Type, Sales Channel, and Vehicle Size contribute to understanding how different factors impact insurance claims. This rich dataset offers valuable insights into customer behavior, policy performance, and overall claim dynamics, making it a robust resource for analyzing trends and patterns in auto insurance claims.

This dataset was initially created in 2011 with values in 2011 dollars. To reflect current economic conditions, I updated it to 2024 dollars using a factor provided by ChatGPT. Additionally, I incorporated index columns to facilitate research and analysis.

This statistic presents the value of incurred losses for private passenger auto insurance in the United States in 2019, by state. In 2019, private passenger auto insurers in Louisiana paid out approximately ************* U.S. dollars in claims.

Telematics Data Platform for Auto Insurance Market Outlook

According to our latest research, the global telematics data platform for auto insurance market size stood at USD 4.2 billion in 2024, reflecting a robust surge driven by the increasing adoption of connected vehicles and data-driven insurance models. The market is projected to expand at a CAGR of 18.6% from 2025 to 2033, reaching a forecasted market size of USD 23.1 billion by 2033. This remarkable growth is primarily attributed to insurersÂ’ growing reliance on real-time data for risk assessment, personalized pricing, and claims management, which is transforming the auto insurance landscape worldwide.

One of the most significant growth factors for the telematics data platform for auto insurance market is the widespread adoption of connected vehicles and the proliferation of Internet of Things (IoT) devices in the automotive sector. As vehicles become increasingly equipped with advanced sensors and communication technologies, insurers are leveraging telematics platforms to collect, process, and analyze vast volumes of data related to driving behavior, vehicle health, and real-time location. This data-centric approach enables insurance companies to develop usage-based insurance (UBI) products, which offer more personalized premiums and foster safer driving habits among policyholders. The demand for such innovative insurance solutions is accelerating as both consumers and insurers recognize the value of telematics in reducing risk, lowering costs, and enhancing customer satisfaction.

Another key driver fueling market growth is the rising emphasis on claims management efficiency and fraud detection. Traditional claims processes are often time-consuming and susceptible to inaccuracies or fraudulent activities. Telematics data platforms enable insurers to access real-time accident data, reconstruct events, and validate claims with greater accuracy and speed. This not only streamlines the claims settlement process but also significantly reduces fraudulent claims, leading to substantial cost savings for insurance providers. Furthermore, the integration of artificial intelligence and machine learning algorithms with telematics platforms enhances predictive analytics capabilities, allowing insurers to proactively identify risk patterns and mitigate potential losses before they occur.

The evolving regulatory landscape and growing consumer awareness about data privacy and transparency are also shaping the growth trajectory of the telematics data platform for auto insurance market. Regulatory bodies across regions are encouraging the adoption of telematics-driven insurance models to promote road safety and responsible driving. At the same time, insurers are investing in robust data security measures and transparent data usage policies to build trust with policyholders. This dual focus on compliance and consumer confidence is fostering the widespread adoption of telematics platforms, particularly in mature insurance markets such as North America and Europe, while also paving the way for rapid growth in emerging markets.

From a regional perspective, North America currently dominates the global telematics data platform for auto insurance market, accounting for the largest share in 2024. This leadership position is underpinned by the high penetration of connected vehicles, advanced digital infrastructure, and the presence of leading insurance technology providers. Europe follows closely, driven by stringent regulatory frameworks and growing consumer preference for personalized insurance products. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid urbanization, increasing vehicle ownership, and the digital transformation of insurance services. As these trends continue to unfold, regional dynamics will play a pivotal role in shaping the future growth and competitive landscape of the telematics data platform for auto insurance market.

The introduction of a Telematics-Enabled Upsell Platform is revolutionizing how insurers approach customer engagement and product offerings. By leveraging telematics data, insurers can identify opportunities to offer additional services and products tailored to individual driving behaviors and preferences. This platform enables insurers to enhance customer satisfaction by providing

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.

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).

Commercial auto insurance providers have displayed robust growth through the current period, leveraging robust e-commerce and trucking activity to provide more comprehensive and varied insurance offerings. Specifically, greater demand for final-mile and less-than-truckload services has encouraged entry in downstream trucking. Additionally, high market inelasticity because of the industry's highly regulated, mandatory nature, has led to notable pricing gains, causing profit to surge at the end of the period. Overall, revenue has climbed at an expected CAGR of 5.8% to $80.1 billion through the current period, including a 1.0% jump in 2025, where profit spiked to 18.0% of revenue. Robust profit growth from the industry's leaders and greater integration of advanced driving systems have driven broader industry profit growth. More specifically, companies have integrated telematics and driver monitoring systems to price and customize policies to the holder's needs. While these incentives have had short-term cost increases for insurers, companies have been able to assess losses more accurately, avoiding major payouts. Additionally, the inelasticity of commercial auto insurance has also promoted strong profit growth. Leading insurers have continued to dominate this industry, leveraging stronger cross-selling abilities to offer broader, more comprehensive insurance offerings. Smaller companies have struggled to compete, with buyers often preferring to pay extra for the added cross-selling convenience. Commercial auto insurance providers will generate stable growth through the outlook period, riding off continued trucking and commercial ride service growth. However, technological innovation may trigger uncertainty. Specifically, autonomous driving is an untested product, potentially introducing the risk of catastrophic failure, software breaches and other issues. Replacement parts are also more expensive in the event of a crash. However, these products present massive long-term savings, with the potential to introduce more accurate telematics to support pricing policies and reduce crash incidence rates over the next decade. This technology closely correlates to the downstream trucking industry's extensive driver shortage, which will continue to raise the risk of a major loss for insurers. Overall, revenue growth will slow, expanding at an estimated CAGR of 1.2% to $85.2 billion through the outlook period, where profit will normalize at 13.3%.

In 2024, losses related to liability insurance claims accounted for roughly ** percent of private passenger auto payouts in the United States. In that year, liability auto insurance losses in the U.S. amounted to approximately *** billion U.S. dollars, and auto insurance losses from physical damage claims amounted to ** billion U.S. dollars.