System design and architecture

 Geico System Design Interview: The Complete Guide

Before diving into fintech-specific problems, it’s important to revisit the essential System Design interview topics that will come up.

  • Scalability and Partitioning: Insurance systems must handle millions of concurrent requests (claims, policy lookups, payments). You’ll need to explain how to scale databases and services using partitioning and sharding strategies.
  • Availability vs Consistency (CAP Theorem): In fintech and insurance, consistency often outweighs availability. Losing or duplicating a transaction is unacceptable. Be ready to explain trade-offs in scenarios like claims adjudication or payments.
  • Load Balancing and Queues: High traffic services like claims intake require load balancers to distribute requests and message queues (Kafka, RabbitMQ) to decouple services.
  • Caching for Performance: To reduce latency, you’ll often cache frequently accessed policy metadata or user session data using Redis or Memcached. But you’ll also need a strategy for cache invalidation to avoid stale financial data.
  • Data Replication and Partitioning: Compliance demands durable storage with backups across regions. You’ll need to explain synchronous vs asynchronous replication and when to prioritize speed over absolute consistency.

Designing a Claims Processing System

One of the most common challenges in the GEICO System Design interview is:

“How would you design GEICO’s claims processing service?”

Key Components

  • Claim Intake: Customer submits a claim via web/app/agent.
  • Verification or Validation: person Identity, policy validity, coverage checks. Fraud detection as well.
  • Approval and Finalization: Rules engine decides approval, partial approval, or rejection.
  • Settlement: Approved claims initiate payment workflows. (payment gateway)
claim intake -> submitted->validation->No Error-> Approval or Finalization-> approved ->Settlement->funded and closed
claim intake -> submitted->validation->No Error-> Approval or Finalization-> Rejected (need to start new claim intake)
Partial approval, we may need to provide the fature to provide the more documentation though the claim intake update with additional documents.

Api for Claim Intake:
 Api to submit claim
 api to upload additonal document to existing claim
api to get and see the claim status



Api for verification and validation:
We don't need api for verification and validation for customer, but we need backup api incase verfication faield to process
api for verification

Approval and Finalization:
Api for to provide primary approval and if needed secondary approval.

Compliance and Auditability

For compliance, every claim must be logged with immutable audit trails, and data must be stored securely with PII encryption.


Latency and Reliability

  • Use queues (Kafka) to decouple claim intake from adjudication.
  • Implement retry logic for external API calls (e.g., third-party verification services).

Trade-Offs

  • SQL vs NoSQL:
    • SQL ensures strong consistency for financial records.
    • NoSQL scales better but risks weaker consistency.
    • A hybrid approach is common: SQL for transactions, NoSQL for metadata/search.
From chatgpt:

Core Features

  1. Submit claim (documents, metadata)

  2. Validate claim (policy, coverage, eligibility)

  3. Detect fraud

  4. Adjudicate claim (rules + manual review)

  5. Approve / Reject / Request info

  6. Process payment

  7. Notify stakeholders

  8. Maintain claim history & audit trail

Non-Functional Requirements

  • High availability (99.9%+)

  • Strong consistency for payments

  • Eventual consistency for analytics

  • Data security (PII, HIPAA / PCI)

  • Auditable (regulatory compliance)

  • SLA-based processing

[ Client / Provider Portal ]
             |
         API Gateway
             |
     ┌───────────────────┐
     │ Claim Ingestion    │
     │ Service            │
     └───────────────────┘
             |
      Message Queue (Kafka/SQS)
             |
 ┌────────────┬─────────────┬─────────────┐
 │ Validation │ Fraud       │ Rules Engine │
 │ Service    │ Detection   │ (Adjudicator)│
 └────────────┴─────────────┴─────────────┘
             |
       Claim Decision Service
             |
     ┌─────────────┬──────────────┐
     │ Payment     │ Manual Review│
     │ Service     │ Workflow     │
     └─────────────┴──────────────┘
             |
       Notification Service
             |
        Audit & Reporting
“I’ll design a scalable claim processing system that allows users or providers to submit claims, validates them against policies, detects fraud, adjudicates claims using business rules, supports manual review, processes payments, and maintains a full audit trail.”

4. Core Components

1. API Gateway

  • Auth (OAuth2 / JWT)

  • Rate limiting

  • Request validation

  • Versioning

2. Claim Ingestion Service

  • Accepts claim payload

  • Stores raw claim

  • Uploads documents to object storage (S3/GCS)

  • Publishes claim event

Data stored:

{
  "claimId": "UUID",
  "policyId": "P123",
  "claimType": "HEALTH",
  "amount": 1200,
  "status": "SUBMITTED"
}

3. Message Queue (Critical)

  • Kafka / AWS SQS

  • Enables:

    • Async processing

    • Retry on failure

    • Loose coupling

4. Validation Service

Validations include:

  • Policy active?

  • Coverage available?

  • Deductibles & limits

  • Duplicate claims

Design Pattern:
✔ Stateless service
✔ Rule-based validation

5. Fraud Detection Service

  • Rule-based checks (thresholds, frequency)

  • ML model (optional)

  • External fraud APIs

Outcome:

  • LOW_RISK

  • MEDIUM_RISK

  • HIGH_RISK → Manual Review

6. Rules Engine (Adjudication)

  • Applies business rules:

    • Coverage rules

    • Co-pay

    • Exclusions

  • Configurable via DB (not code)

Example rule:

IF claim.amount < 500 AND no_fraud THEN AUTO_APPROVE

7. Manual Review Workflow

  • Claims flagged for human review

  • Task assignment

  • SLA tracking

  • Notes & attachments

8. Claim Decision Service

  • Consolidates:

    • Validation result

    • Fraud score

    • Rules outcome

  • Final decision:

    • APPROVED

    • REJECTED

    • NEED_INFO

9. Payment Service

  • Strong consistency required

  • Integrates with banking/payment gateways

  • Idempotent transactions

  • Ledger-based accounting

10. Notification Service

  • Email / SMS / Push

  • Claim status updates

  • Payment confirmation

5. Data Storage Design

Databases

DataStorage
ClaimsRelational DB (Postgres)
DocumentsObject Storage
EventsKafka
AnalyticsData Warehouse
Audit LogsAppend-only store

Claim Table (Simplified)

claim_id (PK)
policy_id
status
approved_amount
created_at
updated_at

6. Consistency & Transactions

  • Saga Pattern for long workflows

  • Idempotency keys for retries

  • Exactly-once payment processing

7. Security & Compliance

  • Encryption at rest & transit

  • Role-based access (RBAC)

  • Tokenized PII

  • Immutable audit logs

  • GDPR / HIPAA compliance

8. Scalability Strategy

  • Stateless services → horizontal scaling

  • Partition claims by claimId

  • Auto-scale workers

  • Cache policy data (Redis)

9. Failure Handling

  • Retry with backoff

  • Dead-letter queues

  • Circuit breakers

  • Manual override for stuck claims

10. Observability

  • Metrics: claim throughput, SLA breaches

  • Logs: per-claim traceId

  • Alerts: payment failures, fraud spikes

11. Extensions (Bonus Points)

  • ML-based adjudication

  • Real-time claim tracking

  • Provider API integration

  • Cross-region DR

  • GDPR “right to forget”

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