Non-Life Automotive Portfolio Claims Data

Columns

• ID: Unique identifier assigned to the policy holder.
• Date_start_contract: The initiation date of the policy contract.
• Date_last_renewal: The date the policy was most recently renewed.
• Date_next_renewal: The scheduled date for the next policy renewal.
• Date_birth: The policy holder's date of birth.
• Date_driving_licence: The date the policy holder obtained their driving licence.
• Distribution_channel: The specific sales channel used to acquire the policy.
• Seniority: A measure of how long the customer has maintained a relationship with the insurer.
• Policies_in_force: The count of active policies currently held by the customer.
• Max_policies: The maximum allowed number of policies for the customer profile.
• Max_products: The maximum number of products associated with the customer.
• Lapse: A binary indicator showing whether the policy has lapsed (0 or 1).
• Date_lapse: The specific date the policy lapsed, if applicable.
• Payment: Details concerning the method of premium payment.
• Premium: The financial cost of the insurance policy.
• Cost_claims_year: The financial outlay incurred for claims during the current year.
• N_claims_year: The number of claims filed within the current year.
• N_claims_history: The total accumulated count of historical claims.
• R_Claims_history: A historical claims ratio or related rate.
• Type_risk: Categorisation of the assessed risk level for the policy.
• Area: Geographic designation of the insured location.
• Second_driver: Indicator variable for whether an additional driver is listed on the policy.
• Year_matriculation: The year the vehicle was first registered.
• Power: The engine power of the insured vehicle.
• Cylinder_capacity: The engine size of the vehicle.
• Value_vehicle: The estimated worth of the vehicle.
• N_doors: The number of doors on the vehicle.
• Type_fuel: The type of fuel the vehicle uses (e.g., Petrol).
• Length: The physical length of the vehicle.
• Weight: The weight of the vehicle.

Distribution

Usage

• Developing predictive models for policy lapse or renewal behaviour.
• Actuarial analysis of motor insurance risk profiles and profitability.
• Evaluating the impact of specific vehicle attributes (such as power or age) on claims frequency and cost.
• Analysing trends in premium setting across different customer and vehicle segments.

Coverage

License

Who Can Use It

• Data Scientists: For training machine learning models that forecast claim events or customer retention.
• Risk Managers: To refine underwriting standards and assess exposure based on vehicle characteristics and customer history.
• Academic Researchers: To study market dynamics within the non-life insurance sector.

Dataset Name Suggestions

1. Motor Vehicle Insurance Policy Transaction History
2. Non-Life Automotive Portfolio Claims Data
3. Historical Customer Records for Car Insurance

Attributes