Dataset

This dataset was created by Bunty Shah

Contents

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.

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 comprehensive auto insurance claims for physical damage in the United States rose to 3.15 per 100 car years in 2021, compared to 2.7 in 2020. This was the highest frequency recorded over the past 15 years.

The DFS ranks automobile insurance companies doing business in New York State based on the number of consumer complaints upheld against them as a percentage of their total business over a two-year period. Complaints typically involve issues like delays in the payment of no-fault claims and nonrenewal of policies. Insurers with the fewest upheld complaints per million dollars of premiums appear at the top of the list. Those with the highest complaint ratios are ranked at the bottom.

Here are a few use cases for this project:

1. Insurance Assessment: This model can be used by insurance companies to automate the process of assessing car damage in insurance claims. By simply using photographs of the damaged vehicle, the model can identify the type and extent of damage, making the claim processing faster and more objective.

2. Automotive Repair Estimates: Car repair shops can use this model to get an approximate idea of the damage and therefore provide a more accurate cost estimate for their clients. It can also assist in identifying nonobvious damage.

3. Used Car Market Evaluation: This model can be used in used car platforms to evaluate the current condition of the cars listed for sale. By identifying existing damage, buyers can make more informed decisions and sellers can price their vehicles more accurately.

4. Law Enforcement and Road Safety: Traffic police and accident investigation teams can utilize this model to evaluate the types of damages after a road accident. It will assist in rebuilding the accident scenario, providing insights during investigations.

5. Auto-manufacturing Quality Control: Automobile manufacturers can use this model in their factories to automatically inspect new cars for any damage or misaligned/missing parts before they are dispatched from the factory, ensuring quality control.

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.

This file contains ultimate claims data taken from the private motor National Claims Iinformation Database (NCID). The claims are grouped together by accident year, the year in which the accident occurred. Not all claims are paid in the lifetime of the policy. Some claims, injury claims in particular, can take many years to be settled and be fully paid. Insurers estimate the cost/number of claims expected for a particular accident year, and this known as the ultimate cost/number of claims. The ultimate cost/number of claims is recalculated regularly, based on the most up-to-date information available. The more time that has passed since the accident year, the more certain the ultimate cost of claims becomes. To view the detailed NCID report kindly refer to the centralbank publication link in the Landing Page section under Additional Info.

This file contains premium data taken from the private motor National Claims Information Database (NCID). Premiums and policy numbers are presented on a “written” and “earned” policy basis and further broken down by different levels of cover - comprehensive and third party. To view the detailed NCID report refer to the Central Bank publication link under Additional Info.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_abcfd12381e7f8d175280d999cdb2dea/view

Here are a few use cases for this project:

1. Automotive Insurance Claims: The model can be used to assist in the processing of car insurance claims by swiftly identifying and categorizing the type and extent of damage to a vehicle after an accident. This could expedite the claims process, reduce human error, and prevent fraudulent reports.

2. Automobile Repair Shops: Mechanics can use the model to rapidly detect and classify damage to vehicles brought into auto body repair shops, helping to save time during inspection and accurately estimate the repair cost.

3. Used Car Sales: Sellers of used cars may use this model to identify all the observable damages on the vehicles before a sale. This benefits both sellers in correctly pricing their cars and buyers by ensuring complete transparency.

4. Vehicle Rental Services: Car rental companies could use the Car Damage V5 model to conduct automated inspections of their vehicles before and after rental periods in order to detect and document any newly incurred damages.

5. Law Enforcement & Accident Analysis: This model can be used by law enforcement agencies or in forensic science to reconstruct accident scenarios. By classifying the type of damage on a vehicle, it could contribute to understanding the nature and cause of a crash.

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.

Here are a few use cases for this project:

1. Auto Insurance Claims Processing: The "damage_car" model can be used by insurance companies to process car claims faster and more accurately. With this tool, personnel would just need to upload pictures to determine the extent and type of damage, hence improving efficiency in settling claims.

2. Vehicle Repair and Maintenance Services: Mechanics and automobile maintenance providers can use this model to automatically classify and evaluate the severity of the damage, helping to provide accurate repair estimates and diagnose the necessary restorations more quickly.

3. Accident Reconstruction: Law enforcement and legal professionals might use this model to understand better the dynamics of road accidents. By classifying car damages, investigators can reconstruct how collisions happened, contributing to court cases and public safety studies.

4. Used Car Marketplace: Online marketplace for used cars can use this model to evaluate the condition of the cars listed for sale. By automatically recognizing damage, potential buyers will have more accurate and useful information when choosing a vehicle.

5. Driver Training Programs: Driving schools could use this application as an educational tool to illustrate to learners the potential outcomes of careless driving. It may use the images as visual aids during driver's education courses or in theory tests.

Description

In the two datasets freMTPL2freq and freMTPL2sev, risk features are collected for 677,991 motor third-party liability policies (observed mostly on one year), in addition to claim numbers by policy as well as their corresponding claim amounts. freMTPL2freq contains the risk features and claim counts, while freMTPL2sev contains claim amounts. Both tables can be linked together via the corresponding policy ID.

Additional information can be found at http://cas.uqam.ca/pub/web/CASdatasets-manual.pdf.

Switzerland Non Life Insurance: Claims Paid: Liability and Motor data was reported at 4,676.000 CHF mn in 2016. This records a decrease from the previous number of 4,802.000 CHF mn for 2015. Switzerland Non Life Insurance: Claims Paid: Liability and Motor data is updated yearly, averaging 4,628.000 CHF mn from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 4,918.000 CHF mn in 2009 and a record low of 3,844.000 CHF mn in 2000. Switzerland Non Life Insurance: Claims Paid: Liability and Motor data remains active status in CEIC and is reported by Swiss Financial Market Supervisory Authority. The data is categorized under Global Database’s Switzerland – Table CH.RG011: Non Life Insurance: Claims Paid.

Here are a few use cases for this project:

1. Insurance Claim Processing: The "3-car-damage-front-right" model can be used by insurance companies to automatically assess the type and severity of damage on the front right side of a car, making the claim processing faster, more accurate, and efficient. This will allow adjusters to focus on other aspects of the claim and improve customer satisfaction.

2. Auto Repair Shop Estimations: Car repair shops can leverage this computer vision model to provide more accurate estimates to clients for repair costs related to paint peeling. The model can help ensure that the repair shop is offering fair pricing to customers and accurately captures the extent of the damage.

3. Online Car Sales: Online platforms for selling used cars can use this model to automatically check and report the paint condition on the front right side of cars up for sale. This can enhance the trust of prospective car buyers, as this transparent and objective evaluation allows them to better understand the car's current condition.

4. Accident Forensics: Investigators can use the "3-car-damage-front-right" model to analyze images from car accidents, helping them determine the severity and pattern of paint peeling caused by the impact. This could assist in better understanding how collisions occurred and provide valuable insights for accident reconstruction.

5. Long-term Car Maintenance Planning: Fleet managers can use the computer vision model to routinely monitor the condition of their fleet's paint, especially in commercial vehicles that experience regular wear and tear. By detecting the severity of paint peeling, fleet managers can make better decisions on when to perform necessary maintenance or repairs, thus extending the lifespan of their vehicles and potentially reducing overall maintenance costs.

freMTPL2 Dataset

This dataset is a mirror of the freMTPL2 frequency and severity datasets, originally published by Arthur Charpentier to accompany his textbook Computational Actuarial Science with R. The freMTPL2 dataset contains data on Third-Party Liability (TPL) Motor insurance policies issued in France, along with claims filed against those policies, observed over a duration of just over a year. These observations are organized into two separate CSV files:

freMTPL2freq.csv: a… See the full description on the dataset page: https://huggingface.co/datasets/mabilton/fremtpl2.

This data set measures and describes participation in PIRP. The researcher may ascertain how many motorists have completed the course and tabulate subsets by: year and month of course completion; motorist residency, age and sex; course provider and delivery method.

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.

Singapore General Insurance Claims: Motor Vehicles data was reported at 638.600 SGD mn in 2016. This records a decrease from the previous number of 651.400 SGD mn for 2015. Singapore General Insurance Claims: Motor Vehicles data is updated yearly, averaging 158.200 SGD mn from Dec 1965 (Median) to 2016, with 52 observations. The data reached an all-time high of 693.400 SGD mn in 2012 and a record low of 8.100 SGD mn in 1965. Singapore General Insurance Claims: Motor Vehicles data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.Z012: General Insurance Statistics.