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.

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.

Dataset

This dataset was created by xiaomengsun

Contents

The frequency of private passenger comprehensive auto insurance claims for physical damage in the United States rose to **** per 100 car years in 2023, compared to *** in 2020. This was the highest frequency recorded over the past 15 years.

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

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.

The frequency of private passenger auto collision insurance claims for physical damage in the United States dropped sharply in 2020 and remained low in 2021, falling to **** per 100 car years. This compares to **** claims per 100 car years in 2019 and *** claims in 2007. Most likely, the primary reason for this decline was the reduction in traffic due to the coronavirus (COVID-19) pandemic. In 2023, insurance claims for physical damage in the United States slightly increased to **** per 100 cars.

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.

In 2022, the net claims incurred by the auto insurance segment in Canada amounted to approximately **** billion Canadian dollars. This is around *** million Canadian dollars higher than the previous year, but almost three times higher than the net auto insurance claims recorded in 1990.

The dataset, named "insurance_claims.csv", is a comprehensive collection of insurance claim records. Each row represents an individual claim, and the columns represent various features associated with that claim.

The dataset is, highlighting features like 'months_as_customer', 'age', policy_number, ...etc. The main focus is the 'fraud_reported' variable, which indicates claim legitimacy.

Claims data were sourced from various insurance providers, encompassing a diverse array of insurance types including vehicular, property, and personal injury. Each claim's record provides an in-depth look into the individual's background, claim specifics, associated documentation, and feedback from insurance professionals.

The dataset further includes specific indicators and parameters that were considered during the claim's assessment, offering a granular look into the complexities of each claim.

For privacy reasons, and in agreement with the participating insurance providers, certain personal details and specific identifiers have been anonymized. Instead of names or direct identifiers, each entry is associated with a unique ID, ensuring data privacy while retaining data integrity.

The insurance claims were subjected to rigorous examination, encompassing both manual assessments and automated checks. The end result of this examination, specifically whether a claim was deemed fraudulent or not, is clearly indicated for each record.

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.

Description This dataset and project are part of ClaimWise AI, an intelligent automation service designed to streamline auto insurance claim processing. All data in this release was collected and curated by our team, ensuring originality and alignment with real-world claim processing scenarios.

What’s inside

• claim_metadata.xlsx: Structured metadata of insurance claims including claim IDs, incident details, vehicle attributes, and fraud indicators.
• Auto-claim service pipeline: Demonstrates how to leverage machine learning, embeddings, and multimodal AI to process claim data and simulate real-world automation.

Note on Images The pipeline references car crash and accident images as part of embedding and similarity checks. These images were also collected by our team from publicly available resources and curated for research purposes. They are not redistributed in this dataset but are used internally to illustrate how ClaimWise AI can handle multimodal data.

Key Features

• Automates claim intake and metadata standardization.
• Uses AI embeddings for fraud detection and duplicate claim checks.
• Supports integration with curated external crash images for damage assessment.
• Provides an end-to-end demo of an AI-powered insurance claim automation workflow.

Use Cases

• Fraud detection in auto claims
• Accident severity classification
• Claim similarity and duplicate detection
• AI-enabled insurance process optimization

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.

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 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.

Germany P&C: Number of Claims: Motor: Vehicle Liability data was reported at 3,489.000 Unit th in 2023. This records an increase from the previous number of 3,357.960 Unit th for 2022. Germany P&C: Number of Claims: Motor: Vehicle Liability data is updated yearly, averaging 3,885.000 Unit th from Dec 1995 (Median) to 2023, with 20 observations. The data reached an all-time high of 4,330.000 Unit th in 1995 and a record low of 3,300.653 Unit th in 2021. Germany P&C: Number of Claims: Motor: Vehicle Liability data remains active status in CEIC and is reported by German Insurance Association. The data is categorized under Global Database’s Germany – Table DE.RG014: Non Life Insurance: Property & Casualty: Number of Claims.

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.

In 2023, the average private passenger auto collision claim for physical damage amounted to ***** U.S. dollars in the United States. This is an increase from the ***** U.S. dollars seen in the previous year and also marked a 15-year high.

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.

Claims Analytics for Auto Insurance Market Outlook

As per our latest research, the global market size for Claims Analytics for Auto Insurance stood at USD 2.85 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.2% projected through the forecast period. By 2033, the market is expected to reach approximately USD 8.37 billion, driven by the increasing adoption of advanced analytics, artificial intelligence, and machine learning technologies in the auto insurance sector. This remarkable growth is primarily fueled by the industry's urgent need to enhance claims processing efficiency, reduce fraudulent activities, and improve customer satisfaction through data-driven insights.

The growth trajectory of the Claims Analytics for Auto Insurance market is largely attributed to the surging volume of auto insurance claims and the rising complexity of claim types. Insurers are increasingly challenged by the need to process vast amounts of unstructured and structured data, which has made traditional methods inefficient and prone to errors. The integration of advanced analytics solutions enables insurers to automate claim validation, streamline processing, and identify anomalies that may indicate fraud. Furthermore, the ongoing digital transformation in the insurance industry, coupled with the proliferation of connected vehicles and telematics, is generating unprecedented data volumes, further necessitating the adoption of robust claims analytics platforms.

Another significant growth driver is the heightened focus on fraud detection and risk assessment. The auto insurance industry faces substantial losses due to fraudulent claims, which not only impact profitability but also erode customer trust. Claims analytics platforms leverage machine learning algorithms, predictive modeling, and big data analytics to detect suspicious patterns and flag potentially fraudulent activities in real time. This proactive approach not only minimizes financial losses but also streamlines the investigation process, allowing insurers to allocate resources more efficiently. As regulatory scrutiny intensifies and compliance requirements become more stringent, the demand for transparent, data-driven claims management processes is expected to surge, further propelling market expansion.

The evolution of customer expectations is also shaping the claims analytics landscape. Modern policyholders demand faster, more transparent, and personalized services, especially during the claims process, which is a critical touchpoint in the customer journey. Claims analytics empowers insurers to deliver tailored experiences by leveraging insights from customer data, historical claims, and behavioral patterns. This enables insurers to provide proactive communication, expedite settlements, and offer customized products, thereby enhancing customer loyalty and retention. The integration of omnichannel communication platforms and self-service portals, supported by analytics, is further transforming the way insurers interact with their clients, making claims management more agile and customer-centric.

Regionally, North America continues to dominate the Claims Analytics for Auto Insurance market, supported by a mature insurance ecosystem, high digital adoption rates, and significant investments in advanced technologies. Europe follows closely, driven by regulatory mandates and the growing adoption of telematics-based insurance products. The Asia Pacific region is emerging as a lucrative market, fueled by rapid urbanization, increasing vehicle ownership, and the digitalization of insurance processes in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as insurers in these regions recognize the value of analytics in optimizing claims operations and improving profitability.

Component Analysis

The Component segment of the Claims Analytics for Auto Insurance market is bi