Motor Insurance Claims
Fake motor insurance claims, including a marked set of fraudulent ones, for testing fraud detection.
- Rows
- 150
- Region
- India
- Labeled PII
- Yes
- Fraud labels
- Yes
What you can test with it
- Score a DLP, redaction or PII-detection system against the ground-truth answer key — every sensitive field is already labeled.
- Evaluate fraud detection: a labeled fraudulent subset is included, so precision and recall are measurable rather than estimated.
- Test motor-insurance claims processing and fraud detection on India-shaped data rather than US-shaped placeholders.
- Populate a lower environment, demo or training set without touching real customer data.
Fields
- claim_ref
- policy_ref
- vehicle_ref
- insurer_name
- policyholder_ref
- claimant_ref
- driver_ref
- driver_relationship
- adjuster_ref
- surveyor_ref
- claim_number
- incident_date
- claim_filed_date
- place_of_loss
- loss_type
- police_report_filed
- fir_number
- police_station
- other_insurance_details
- repair_workshop_name
- claimed_amount_minor
- assessed_amount_minor
- status
- is_fraud
- fraud_signal
- fraud_evidence
- make
- model
- year
- registration_number
- sum_insured_minor
- premium_minor
- policy_status
- region_code
- country_code
Related datasets
Is this data safe to use?
Yes. Every record is generated, never derived from or masked from real people, and every identifier is non-issuable by construction — correctly formatted, but failing checksum validation or using a reserved range so it can never match a real person or institution.