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.

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 variable.

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.

Dataset

This dataset was created by xiaomengsun

Contents

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.

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.

This dataset contains 9470 rows of insured data from vehicle insurance (2000-2004 cohort), where the following attributes consist of:

• POL_ID: policy id/number of policyholder
• INS_AGE: age of policyholder
• INS_GENDER: gender of policy holder (female=f, male=m)
• POL_EFF_DT: the date on which the policy enters into force/activated
• POL_TRMNT_DT: the date on which the policy ends which the policy ends
• CAR_USE: policyholder's car usage (private/commercial)
• CAR_TYPE: policyholder's car type (minivan, suv, van, sports car, truck, pickup)
• INS_AREA: the city community where the policyholder lives (urban/rural)
• CLM_FREQ: number of claims during the period
• TOTAL_CLM_AMNT: total claim amount during the period

There are no missing or undefined values in the dataset.

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.

Recent five years property insurance market any motor vehicle insurance premium income statistics - Self-use large passenger car (Bao Fa Center)

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.

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.

In the United Kingdom, younger drivers paid more on average for their car insurance than older drivers in 2024. A driver who is around 20 years old would be charged roughly *** British pounds whereas a driver in their 30s would be charged an average rate of *** British pounds. This higher premium stems from the idea that young drivers engage in more risky driving behavior, such as drunk driving, and therefore pose a higher risk to insurance companies. Young drivers pay more, but also tend to have more coverage Prices of different car insurance cover plans in the UK have increased since early 2022 and exceeded *** British pounds in 2023. Third party, fire and theft plans overall had higher premiums than comprehensive plans. This, however, is because the basket of risks reflects the type of driver that buys such cover, which is typically young drivers.

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

The United States Car Insurance Market is Segmented by Coverage Type (Liability, Collision, Comprehensive, and More), Application (Personal Vehicles, Commercial Fleet), Distribution Channel (Direct-To-Customer, Intermediated, Embedded), and Region (Northeast, Midwest, South and West). The Market Forecasts are Provided in Terms of Value (USD)

One of the perceived future benefits of buying insurance online, other than more individualized policies, faster processing and risk assessment and a better overall customer experience is that, with the use of digital data, average premiums for individuals will fall. One way in which insurtech companies are looking to improve on mitigating risks and enhancing customer experience in the motor insurance industry is through telematics. Telematics is an offset of information technology that deals with the transmission of digital computerized information. Already, motor insurance is becoming less about customer demographics but by individual behavior, using real time data collected from your vehicle. Where and when you drive, what the traffic is like where you drive, how fast and often you drive are some of the data points that can determine how much you will pay for car insurance in the near future. Statista estimates that the overall average cost of online premiums for motor insurance will decrease between 2018 and 2024. To learn more about the future of the B2C digital insurance industry in the UK, read our in-depth report.

In 2024, households in Japan spent an average amount of about ****** Japanese yen on voluntary automobile insurance premiums. The average expenditure on voluntary automobile insurance per household declined for the fourth consecutive time.

The data are real automobile insurance data from a Norwegian company in the late 1980's and early 1990's. The source is confidential. The example comes from a Norwegian insurance company, was shared with Erik Bølviken and consists of n=6446 claims of cost due to personal injuries in motor insurance with the deductible subtracted. They include cost due to personal injuries. They have mean and standard deviation 23.9 and 28.9 in 1000 NOK, and are heavy-tailed with skewness 5.6 and kurtosis 71.2.

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.

Dataset Description:

Car insurance is required for drivers in almost every state. It is not a requirement in New Hampshire for drivers to buy car insurance, but drivers there do need to show proof that they can afford to pay the cost of an accident if it’s their fault. Most drivers have car insurance because it is the law, but that doesn’t mean you should only buy the minimum required coverage.

The vehicle insurance dataset was obtained from the Ethiopian Insurance Corporation. It is a large dataset that can be used for machine-learning purposes.

We have 2 csv file. Both with the same columns name and different in values. You can create some interesting machine learning algorithm and analysis for this valuable topic. So, go ahead!

131 scholarly articles cite this dataset

Author: Edossa Terefe

Automobile insurance subscription information is data that comprehensively provides major contract statistics related to automobile insurance, information on damage status, and information on the status of automobile accident victims. The data consists of the following three operations. ① Automobile insurance contract information inquiry: Function to search contract statistics such as insurance type, collateral type, gender, age, foreign products, vehicle type, number of subscriptions, and earned insurance premiums. ② Automobile insurance loss status information inquiry: Function to search insurance type, collateral type, vehicle type, amount of damage, number of injuries/partial losses, number of deaths/total losses, etc. ③ Automobile insurance victim information inquiry: Function to provide statistical information on death injury type, disability status, injury grade, disability grade, and number of people.