Health Insurance Claims
Fake health insurance claims, including a marked set of fraudulent ones.
- Rows
- 120
- 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 health-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
- insurer_name
- patient_ref
- treating_doctor_ref
- claims_processor_ref
- hospital_name
- hospital_city
- hospital_state
- admission_date
- discharge_date
- diagnosis
- claim_type
- hospitalization_reason
- injury_cause
- medico_legal
- police_report_filed
- fir_number
- other_insurance_details
- prior_hospitalization_disclosed
- is_network_hospital
- pre_authorization_obtained
- pre_authorization_number
- doctor_registration_number
- discharge_status
- claimed_amount_minor
- approved_amount_minor
- status
- is_fraud
- fraud_signal
- fraud_evidence
- 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.