Geico interview question System design:

 Geico System Design Interview: The Complete Guide

System Design Interview Questions and Answers - GeeksforGeeks

System Design Basics Refresher

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

• Claim Submission: Allow users (claimants, agents) to submit claims online/via app with attachments (photos, documents).
• Claim Validation & Routing: Automatically check for completeness, flag errors, and route to appropriate adjusters/departments.
• Workflow Automation: Manage claim stages (review, investigation, approval, denial, payment).
• Payment Processing: Integrate with financial systems for secure payment issuance (EFT, check).
• Reporting & Analytics: Generate reports on claim volume, processing times, fraud detection, and financials.
• User Management: Role-based access control (admin, adjuster, claimant, auditor).
• Communication: Automated notifications (email, SMS) for claim status updates.
• Fraud Detection: Implement rules or AI to flag suspicious claims for manual review. 

Non-Functional Requirements (How the system performs)

These define the quality attributes and constraints of the system.

• Performance:
  • Page load times < 2 seconds.
  • Process 10,000 claims/hour during peak.
• Security:
  • Data encryption (at rest & in transit).
  • Authentication (MFA) & Authorization.
  • Audit trails for all data access/changes.
• Scalability: Handle 2x anticipated claim volume without performance degradation (horizontal scaling).
• Reliability/Availability: 99.9% uptime.
• Usability: Intuitive UI, minimal training required for basic tasks.
• Compliance: Adherence to HIPAA (health), GDPR (data privacy), state/federal insurance regulations.
• Maintainability: Modular design, clear documentation, easy updates.
• Portability: Accessible across major web browsers and mobile devices

APIs for Customers and Agents

You’ll need REST or gRPC APIs that allow customers to:

• Submit claims.
• Track claim status.
• Upload supporting documents.
  Agents may require internal APIs for escalations and overrides.
• Validate the claim
• Process the claim
• Payment api

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.

Customer → Claims API → Queue (Kafka) → Verification Service → Adjudication Engine → Settlement Service → Payment System

Building a modern, event-driven application for insurance claims processing – Part 1 | AWS for Industries

Building a modern, event-driven application for insurance claims processing – Part 2 | AWS for Industries

Fraud Detection in Insurance

1. Functional Requirements (What the system does)

• Real-Time Anomaly Detection: Identify suspicious transactions and claims instantly to prevent immediate financial loss.
• Cross-Carrier Analysis: Automatically scan and compare claims data (e.g., VINs, damage photos) against other participating carriers to detect duplicate filings for the same incident.
• AI Damage Assessment: Use computer vision to evaluate damage photos for inconsistencies, anomalies, or "photo reuse" from previous claims.
• Behavioral Monitoring: Track digital footprints in real-time, including typing cadence, device pressure, and swipe patterns to distinguish between legitimate users and automated bots.
• Dynamic Risk Scoring: Generate a fraud score (e.g., "Smart Red Flag") that adjusts based on customizable parameters like odometer disparities or garaging errors.
• Automated Case Management: Route high-risk claims automatically to the Special Investigations Unit (SIU) while generating investigation summaries via Generative AI.
• Identity & Document Verification: Verify the authenticity of driver's licenses, medical bills, and other claim-related documents in real-time. 

2. Non-Functional Requirements (How well it performs)

• Latency (Performance): Machine learning models must provide fraud predictions within milliseconds to support "straight-through" claims processing without hindering user experience.
• Scalability: The system must handle high volumes of transaction data—potentially millions of records—without performance degradation.
• Compliance (Regulatory): Adhere to evolving 2026 standards, including GDPR, HIPAA, and new Nacha ACH fraud monitoring requirements effective June 2026.
• Model Explainability: Provide transparent "reason codes" for why a claim was flagged to ensure decisions are auditable and legally defensible.
• Security (Zero-Trust): Implement Multi-Factor Authentication (MFA) and the principle of least privilege to prevent unauthorized system access.
• Reliability: Ensure 24/7 availability with minimal downtime, utilizing redundant storage to prevent data loss.
• Privacy-Preserving AI: Protect sensitive customer data at ingestion using tokenization, masking, and federated learning techniques to train models without exposing raw data. 

Core Techniques

• Rule-Based Detection: Traditional if-then rules like “Flag claims above $50,000 submitted within 24 hours of policy creation.” Rule-based approaches are easy to implement but rigid and often fail to detect evolving fraud patterns.
• ML-Driven Detection: Machine learning models trained on historical claims can detect subtle anomalies (e.g., unusual claim patterns, repetitive IP/device activity). ML-based detection improves adaptability but requires feature pipelines and regular retraining.

Real-Time Monitoring

• Event Streams (Kafka): Every claim event is published into a Kafka stream.
• Processing Engines (Spark/Flink): Fraud services consume streams, apply models, and flag suspicious activity.
• Alerts: Flags trigger downstream services for manual review.

Device Fingerprinting and Profiling

• Track device/browser metadata to identify repeat fraud attempts.
• Build user and household profiles to detect unusual activity like multiple claims from the same device.

Data Storage

• Hot Storage: Redis/NoSQL for storing recent fraud signals with fast access.
• Cold Storage: Data lake (S3, Hadoop) for long-term fraud analytics, retraining ML models, and compliance audits.

Trade-Offs: Accuracy vs Performance

• Higher accuracy requires complex ML models, but may slow down real-time decisions.
• Lighter models or rule-based detection offer speed but increase false positives.
• Best approach: Hybrid pipeline—fast rules for immediate checks, ML models for secondary analysis.

Data Pipelines and Reporting

At scale, GEICO processes billions of policy and claim records annually. That’s why interviewers often ask:

“How would you design GEICO’s data pipelines for compliance and analytics?”

ETL Pipelines

• Extract: The first stage in the ETL process is to extract data from various sources such as transactional systems, spreadsheets, and flat files. This step involves reading data from the source systems and storing it in a staging area.
• Transform: In this stage, the extracted data is transformed into a format that is suitable for loading into the data warehouse. This may involve cleaning and validating the data, converting data types, combining data from multiple sources, and creating new data fields.
• Load: After the data is transformed, it is loaded into the data warehouse. This step involves creating the physical data structures and loading the data into the warehouse.

Functional Requirements

These define the specific tasks and operations the data pipeline must perform.

• Multi-Source Data Ingestion: Automate the ingestion of diverse datasets, including core insurance systems (policy, claims, billing), CRM platforms like Salesforce, and high-volume IoT/telematics data.
• Real-Time and Batch Processing: Support both low-latency stream processing (e.g., for immediate fraud detection or real-time driver feedback) and scheduled batch processing for historical risk modeling.
• Automated Data Validation and Cleansing: Implement automated checks to ensure data accuracy, completeness, and consistency before it reaches downstream analytics platforms.
• Regulatory Reporting Generation: Automatically generate standardized compliance reports and audit trails for agencies and regulatory bodies.
• Data Lineage Tracking: Maintain a clear, visual record of data flow from origin to final report to satisfy audit requirements and facilitate root-cause analysis.
• Personalized Analytics Support: Provide structured data models that allow for "hyper-personalized" policy pricing and behavioral underwriting based on customer data.

Non-Functional Requirements

These define how the system operates and its quality attributes, ensuring long-term reliability and trust.

• Security and Privacy: Enforce zero-trust principles and robust encryption for all data at rest and in transit, specifically protecting sensitive PII (Personally Identifiable Information).
• Scalability: Utilize cloud-native, auto-scaling infrastructure (such as Azure or AWS with Kubernetes) to handle sudden spikes in telematics or claims data during major weather events.
• Compliance Adherence: Ensure the system dynamically adapts to evolving regulations like GDPR, CCPA/CPRA, and state-specific insurance mandates through an AI-powered rules engine.
• Reliability and High Availability: Maintain near-constant uptime (e.g., 99.9%+) to prevent disruptions in real-time underwriting and claims processing.
• Data Governance and Access Control: Implement fine-grained, role-based access control (RBAC) to ensure only authorized personnel and AI agents can access specific data subsets.
• Interoperability: Ensure the pipeline uses standardized protocols (like REST APIs or GraphQL) to seamlessly connect legacy mainframe systems with modern AI and ML platforms.

Batch vs Real-Time Pipelines

• Batch (Hadoop, Airflow, Spark): Used for compliance reports and daily dashboards.
• Real-Time (Kafka, Flink): For fraud detection alerts, live claim monitoring, and customer notifications.

Use Cases

• Compliance Audits: Regulators may request transaction records for past 7–10 years.
• Risk Reports: Identify high-risk customers or fraudulent activity trends.
• Merchant Dashboards: Provide visibility into settlements and policy activities.

Data Warehousing Considerations

• Redshift, Snowflake, BigQuery for analytics.
• Must enforce row-level security to protect PII.
• Data Lake (S3/HDFS) for raw storage of long-term historical records.

Caching and Performance Optimization

Insurance systems are query-heavy, making caching a critical performance strategy in the GEICO System Design interview.

Reducing Latency

• Frequent lookups: claim details, policy status, customer profiles.
• Instead of hitting SQL databases repeatedly, cache results in Redis or Memcached.

Types of Caching

• Metadata Caching: Frequently accessed data, like policy summaries or claim status.
• Session Caching: Store user and agent session tokens in Redis to avoid repeated authentication lookups.

Cache Invalidation Strategies

• Time-Based (TTL): Expire cache entries automatically. Useful for claim lookups.
• Event-Based: Invalidate cache when a policy or claim changes. Example: If a claim is approved, update cache immediately.

How Amazon RDS scales up automatically when data size increases:

When data size increases in Amazon RDS, storage automatically scales up by default (in increments like 10% or 5GB) to prevent full storage, often with minimal downtime using EBS Elastic Volumes, but you can manually scale or use read replicas for performance; for major growth, sharding (splitting data) might be needed, while compute scaling (instance size) usually causes brief unavailability. 

Storage Scaling

• Automatic Storage Scaling: RDS monitors free space and automatically adds storage (e.g., 10% of current size, 5GB, or predicted growth, whichever is largest) if it drops below 10% for 5 mins, preventing storage-full errors.
• EBS Elastic Volumes: RDS uses Amazon EBS, allowing storage size/type changes on-the-fly, often with performance degradation only on older volumes, notes AWS re:Post.
• Manual Scaling: You can manually increase storage via the AWS console, which might cause a brief outage, especially if you're not using Multi-AZ. 

Performance Scaling (Beyond Storage)

• Read Replicas: Create read-only copies of your primary DB to offload read traffic, improving performance for read-heavy apps.
• Multi-AZ Deployments: For high availability and less downtime during scaling/failures, use Multi-AZ, which keeps a synchronous standby in another Availability Zone.
• Compute Scaling (Instance Size): Increasing the instance class (CPU/RAM) requires a brief restart, causing temporary unavailability, say AWS documentation. 

Advanced Scaling

• Sharding: For extreme growth, split your data across multiple smaller databases (shards) based on a key (like tenant_id), improving per-tenant performance and manageability, according to AWS blog. 

For system design interview questions:

Start discussing with functional and non functional requirement. spend most of the time to discuss on it

for some of the functional requirements enhanced or limited or skip for time being. (nice to have feature.)

once you have both start with high level design: go with block 
modify high level design with scalability and resiliency and caching option. still its a high level design

outline load balancer here.

before high level, if possible , work for traffic estimate, storage estimate and network speed 

while making high level , breakdown component or service module to be in high level. Talk about the api

restful or soap, request and resposne format json string etc. this can be in low level design as well.

talk about the database and the scope of the database that we use in the system.

Talked about the traditional message queue vs modern kafka style event driven technology.

Talked the caching strategy + redis vs inmemory cache.

talked about the redis( distributed cache) is intra service cache whereas local is inmemory cache. use both

Talked about the low level design, define user scope , different user and their scopes.

Talked about ETL processing as well.

Identify risks and implement measures like authentication, encryption, and data protection.

At the end talk about the DR sites, disaster recovery sites for productions failover mechanism.

Implement the monitoring and alerting tools. datadog alert and monitoring is the best.

Step 2: Capacity Estimation
1. Traffic Estimates

• Users: To forecast how many DAUs are there at a particular time.

• Requests: Guess the number of requests per second during their high loads.

2. Storage Estimates

• Data Volume: calculate how much data is being produced per day, per month, and per year.

• Data Growth: The future growth rates should also be counted.How to Answer a System Design Interview Problem/Question? - GeeksforGeeks

3. Network Bandwidth

System Design Introduction - LLD & HLD - GeeksforGeeks

• Component/module breakdown: Detailed internal logic for each module—with class responsibilities, methods, attributes, interactions
• Database schema & structure: Designing tables, keys, indexes, relationships with SQL/NoSQL refinements
• API & interface definitions: Precise request/response formats, error codes, methods, endpoints, and internal interfacing
• Error handling & validation logic: Define how each module manages invalid inputs, failures, edge cases, and logging
• Design patterns & SOLID: Implement design patterns and solid principles to ensure clean, extensible, maintainable code
• UML and pseudocode artifacts: Class diagrams, sequence diagrams, pseudocode or flowcharts to clarify logic paths and method calls

Common examples of NoSQL databases consist of:

• MongoDB: A popular document shop that is flexible and scalable.
• Cassandra: A wide-column shop recognised for its ability to address huge amounts of information and excessive write throughput.

Common examples of SQL databases are:

• MySQL: An open-source relational database this is widely utilized in diverse packages.
• PostgreSQL: A powerful open-source relational database known for its extensibility and assist for advanced functions.

• SQL vs. NoSQL - Scalability and Performance

  • Vertical Scaling in SQL : SQL databases traditionally scale vertically by adding more resources to a single server, but this has limitations.
  • Horizontal Scaling in NoSQL : NoSQL databases shine in horizontal scaling, distributing data across multiple servers to handle increasing loads seamlessly.
  • 
    

SQL is : Suitable for complex transactions, strict data integrity, and well-defined relationships.

  Standardized SQL language for querying data.

No SQL is : Ideal for applications demanding high scalability, handling large volumes of unstructured data, and rapid development cycles.

Varied query languages, with some using SQL and others adopting unique approaches.

Decision Factors for System Design	Align choice with specific project requirements, considering data structures, scalability needs, and development pace.	Evaluate team expertise in SQL or NoSQL, and consider long-term scalability and adaptability aligned with project growth.

so use the right choice of database based on the cost, operation cost and team expertiese.

Database Sharding 

Database sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database.

horizontal sharding, vertical sharding or range base sharding you can use it based on the 

Database Sharding - System Design - GeeksforGeeks