Design a Fraud Detection System

Functional Requirements

1. Real-time Transaction Monitoring:

   • The system should monitor transactions in real-time to detect potential fraudulent activities.
   • It should support various types of transactions, including credit card, bank transfers, and online payments.

2. Fraud Detection Algorithms:

   • Implement machine learning models to identify patterns indicative of fraud.
   • Support rule-based detection for known fraud patterns.

3. Alert Generation:

   • Generate alerts for transactions flagged as potentially fraudulent.
   • Alerts should be sent to relevant stakeholders, such as fraud analysts or automated systems for further action.

4. User and Transaction Profiling:

   • Maintain profiles for users and transactions to identify anomalies.
   • Update profiles dynamically based on new data.

5. Reporting and Analytics:

   • Provide dashboards and reports for monitoring fraud trends and system performance.
   • Allow for custom report generation based on specific criteria.

6. Integration with External Systems:

   • Support integration with external data sources for enhanced fraud detection.
   • Provide APIs for integration with other financial systems.

7. Scalability:

   • The system should handle increasing volumes of transactions without degradation in performance.

8. Audit and Logging:

   • Maintain logs of all transactions and detection activities for audit purposes.
   • Ensure logs are secure and tamper-proof.

Non-Functional Requirements

1. Performance:

   • The system should process transactions with minimal latency to ensure timely detection.
   • Aim for sub-second response times for transaction processing.

2. Reliability:

   • Ensure high availability with minimal downtime.
   • Implement failover mechanisms to handle system failures.

3. Scalability:

   • Design the system to scale horizontally to accommodate growing transaction volumes.
   • Support load balancing to distribute traffic efficiently.

4. Security:

   • Implement robust security measures to protect sensitive data.
   • Ensure data encryption both at rest and in transit.

5. Accuracy:

   • Minimize false positives and false negatives in fraud detection.
   • Continuously improve detection algorithms based on feedback and new data.

6. Extensibility:

   • Design the system to easily incorporate new detection algorithms and data sources.
   • Allow for easy updates and enhancements without major system overhauls.

7. Compliance:

   • While not a primary focus, ensure the system can be adapted to meet regulatory requirements if needed.

8. Monitoring and Alerting:

   • Implement monitoring tools to track system health and performance.
   • Set up alerts for system anomalies or failures.