Approach: Clarify trading volume and latency requirements, then propose distributed architecture using time-series database (Kafka for streaming, TimescaleDB for storage) with write-ahead logging for durability.

Considerations: Balance consistency and availability with eventual consistency for read replicas while maintaining strong consistency for critical trade records. Factor in regulatory audit trails and reconciliation windows.

Key Points: Handle millions of trades per second with sub-millisecond latencies. Use partitioning strategies by currency pair and asset class. Integrate with JPMorgan's risk control frameworks to prevent erroneous trades.

Approach: Establish scope by identifying affected pricing feeds, markets, and clients. Isolate the problem layer and use structured logging and metrics to correlate timing and market conditions.

Considerations: Prioritize minimizing trader and client impact by rolling back, switching to backup source, or serving stale prices. Balance speed against root cause accuracy and regulatory compliance documentation.

Key Points: Debug under pressure using APM tools and distributed tracing. Cross-check against exchange feeds and internal benchmarks. Conduct post-mortems that prevent recurrence without blocking innovation.

Approach: Design sliding-window anomaly detector using statistical baselines (velocity, amount, geography) and ML scoring (isolation forests, neural networks). Flag high-confidence cases for real-time review without blocking legitimate transfers.

Considerations: Balance false positives (customer friction, regulatory reporting) against false negatives (fraud leakage). Account for seasonal patterns and legitimate spikes like end-of-month corporate transfers.

Key Points: Implement sub-100ms latency for real-time fraud decisions. Incorporate JPMorgan's proprietary customer behavior data. Ensure BSA/AML compliance with audit logs capturing every decision for regulatory examiners.