Core Entities
Policy: (policy_id, policyholder_id, policy_type=AUTO|HOME|HEALTH|LIFE, coverage_amount_cents, premium_cents, deductible_cents, start_date, end_date, status=ACTIVE|EXPIRED|CANCELLED). Claim: (claim_id, policy_id, claimant_id, incident_date, reported_date, claim_type, description, claimed_amount_cents, status, assigned_adjuster_id). ClaimDocument: (doc_id, claim_id, doc_type=PHOTO|RECEIPT|POLICE_REPORT|MEDICAL_RECORD, url, uploaded_at). ClaimAssessment: (assessment_id, claim_id, adjuster_id, approved_amount_cents, denial_reason, notes, created_at). Settlement: (settlement_id, claim_id, payment_amount_cents, payment_method, payment_date, status).
Claim Submission and Validation
On claim submission: validate policy is active at the incident_date (incident must occur within policy period). Check policy type matches claim type (cannot file auto claim on home policy). Verify claimant is the policyholder or a listed beneficiary. Check for duplicate claims (same incident_date, same policy — prevent double-filing). Auto-assign a claim number (formatted: CLM-{year}-{sequence}). Set initial status=SUBMITTED. Apply first-pass validation rules (claimed_amount > coverage_amount: flag; incident_date > reported_date: flag). Acknowledge receipt via email with the claim number and expected processing timeline.
Adjuster Assignment and Review Workflow
Claims are routed to adjusters based on claim type and complexity. Simple claims (auto, amount $50K, disputed liability, health claims): assign to a specialist adjuster. Assignment: load-balance by active claim count per adjuster, considering specialization. Status machine: SUBMITTED -> UNDER_REVIEW (adjuster assigned) -> ADDITIONAL_INFO_REQUIRED (adjuster requests more documents) -> ASSESSMENT_COMPLETE (adjuster makes a decision) -> APPROVED or DENIED -> SETTLED (payment processed). Each transition creates an audit log entry with actor, timestamp, and notes.
Auto-Adjudication
Many simple claims can be decided automatically using rule-based systems. Rules engine: define decision trees for each claim type. Auto-approve criteria: claim amount <= deductible + some threshold, no prior claims in the last 12 months, all required documents present, incident is in a low-risk category. Auto-deny criteria: policy was expired at incident date, claimed amount exceeds coverage, required documents missing after 30 days. Auto-adjudication reduces human review load by 60-80% for standard claims. Track auto-adjudication accuracy: sample auto-approved claims for manual review to detect rule gaps.
Fraud Detection
Fraud signals: (1) Velocity: multiple claims in a short period (3+ claims in 6 months from the same policyholder). (2) Amount patterns: claimed amount always just below the auto-approve threshold. (3) Document quality: uploaded photos show metadata from a different date than the claimed incident date. Check EXIF data on images. (4) Network analysis: same repair shop on 10+ different claims — potentially colluding with claimants. (5) Address/device clustering: multiple claims submitted from the same IP address or device. Scoring: each signal contributes to a fraud risk score (0-100). Score > 70: flag for manual review. Score > 90: auto-deny and notify the special investigations unit (SIU). Store fraud_score and fraud_signals[] on the claim record.
Settlement and Payment
On claim approval: calculate settlement amount = min(approved_amount, coverage_amount) – deductible. Create a Settlement record. Process payment via ACH (bank transfer) or check. Track payment status: PENDING -> PROCESSING -> PAID. For health insurance: payer-provider model — pay the healthcare provider directly, not the claimant. Subrogation: if a third party is at fault (car accident caused by another driver), the insurance company pays the claimant and then seeks reimbursement from the at-fault party’s insurer. Track subrogation opportunities on the claim record.
Interview Tips
- The claim status machine has many states — draw it explicitly. Missing a state transition (like ADDITIONAL_INFO_REQUIRED -> UNDER_REVIEW on document resubmission) shows incomplete thinking.
- Auto-adjudication is the key scalability feature. Without it, every claim requires a human adjuster — not scalable at millions of claims per year.
- Regulatory compliance: in most jurisdictions, insurers must acknowledge claims within 24 hours and make a decision within 30 days. Build SLA tracking into the system (analogous to the customer support SLA design).
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“text”: “Claim status transitions: SUBMITTED (claim received) -> UNDER_REVIEW (adjuster assigned or auto-adjudication started) -> ADDITIONAL_INFO_REQUIRED (adjuster requests documents) -> UNDER_REVIEW (documents received, review restarts) -> ASSESSMENT_COMPLETE (decision made) -> APPROVED or DENIED -> SETTLED (payment processed for approved claims). Additional states: WITHDRAWN (claimant withdraws the claim), APPEALED (claimant disputes a denial), CLOSED (final state after settlement or appeal resolution). Each transition must be logged with actor (user or system), timestamp, and reason. Invalid transitions are rejected (cannot go from SETTLED back to UNDER_REVIEW). Implementation: a state machine table defines allowed (from_status, to_status) pairs. The application validates transitions before applying them.”
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“name”: “How do you implement auto-adjudication for insurance claims?”,
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“text”: “Auto-adjudication processes simple claims without human review. Rules engine: define decision trees per claim type. Auto-approve conditions (AND logic): policy is active at incident date, claimed amount <= coverage – prior_claims_paid (within remaining coverage), claimed amount <= auto_approve_threshold (e.g., $2,500 for auto glass), all required documents present (completeness check), no prior claims in last 6 months (first-time claimant), fraud score coverage, required documents missing after 30 days. If neither auto-approve nor auto-deny: escalate to a human adjuster. Track auto-adjudication accuracy: sample 5% of auto-approved claims for manual review. If override rate > 5%, revisit the rules.”
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“@type”: “Question”,
“name”: “How do you detect fraudulent insurance claims?”,
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“text”: “Fraud detection uses a scoring model combining multiple signals. Velocity signals: 3+ claims from the same policyholder in 12 months scores +20. Amount patterns: claimed amount within 5% of the auto-approve threshold (suspiciously precise) scores +15. Document signals: photo EXIF metadata shows a date different from the claimed incident date scores +30. Network signals: same repair shop or medical provider on 5+ claims from different policyholders scores +25 (provider fraud pattern). Geographic signals: incident reported in a different state than the policy’s service area scores +10. Device/IP signals: multiple claims submitted from the same device or IP in 24 hours scores +20. Score thresholds: 0-40 = low risk (auto-adjudicate). 40-70 = medium risk (flag for adjuster review). 70+ = high risk (auto-deny + SIU referral). Retrain the scoring model quarterly on confirmed fraud cases.”
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“text”: “Documents required vary by claim type: auto claims need photos of damage, police report (if accident), repair estimate. Health claims need EOB (Explanation of Benefits), receipts. Upload flow: client receives a presigned S3 URL, uploads directly to S3. On upload: S3 triggers a Lambda that extracts metadata (file type, size, EXIF data for photos) and creates a ClaimDocument record. Document validation: check file type is allowed (PDF, JPG, PNG — no executables). Check file size limit (20MB). For photos: extract EXIF date and compare to incident date (fraud signal). Optical character recognition (OCR): run AWS Textract or Google Document AI to extract text from uploaded receipts and police reports. Auto-populate claim fields from extracted text (date, amount, parties involved). Store extracted text for full-text search on claims.”
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“text”: “On claim approval: calculate settlement_amount = min(approved_amount, remaining_coverage) – deductible. If settlement_amount PAID. For check: generate a physical check via a check printing service. Mail to address on file. Settlement_status = MAILED -> CASHED (when bank confirms encashment). For health claims (provider payment): pay the healthcare provider directly via EFT. Track Explanation of Payment (EOP) document sent to the claimant. All payments are logged immutably for regulatory compliance.”
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