Real-Time Risk Analytics Platform
Client Profile
Mid-size asset manager, $15B AUM
Building a real-time risk monitoring system to replace end-of-day batch processing.
The Challenge
The client's risk team was working with data that was always at least 12 hours old. In volatile markets, this meant making decisions based on stale information. Their existing system was a collection of Excel spreadsheets and overnight batch jobs that took 6 hours to complete.
Why a Long-Term Partner
Previous attempts with Big 4 consultants had produced detailed requirements documents but no working software after 8 months and significant spend. The client wanted a team that would build incrementally and demonstrate progress in weeks, not quarters.
Our Approach
- Started with single risk metric to prove real-time capability
- Built streaming data pipeline before touching calculation logic
- Migrated calculations incrementally, validating against existing outputs
- Maintained parallel systems until business users confirmed accuracy
Technical Solution
Kafka-based streaming platform with custom calculation engine in Rust for performance-critical paths. React dashboard with sub-second updates. PostgreSQL for historical analysis, TimescaleDB for time-series data. Deployed on-premises per regulatory requirements.
Why Continuity Matters
Financial regulations change frequently, and new risk metrics need to be added as the business evolves. Having a team that understood both the technical architecture and the business context meant we could implement new requirements without architectural rework each time.
- Reduced risk calculation latency from 12 hours to under 30 seconds
- Enabled intraday position adjustments previously impossible
- Automated 80% of manual reconciliation work
- Passed regulatory audit on first attempt
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