Aggregation at Scale and Millisecond VaR: Fast, Accurate, Explainable

Risk teams are being asked for more: intraday answers instead of EOD, easy and fast backtesting and iterative stress tests across long histories, and full drill-down to sensitivities and VaR, with full explainability and decomposition, time travel, and auditability of amendments. The blocker has always been performance at scale, forcing compromises on accuracy, speed, and transparency.

In this 30-minute webinar, see how Opensee enables interactive risk metrics at full data granularity and history without shortcuts. We’ll share benchmarks that deliver the trifecta every risk team needs - accuracy, speed, and transparency - calculating hundreds of thousands of VaRs in seconds on AWS compute at under $3/hr.

What you’ll see:

• VaR benchmarks on long P&L vectors (up to 2008), on very long history (up to 2 years) as well as detailed sensitivities
• Interactive computation of risk metrics on full-history, full‑granularity data
• Ability to scale and resist to high concurrency
• How explainability is maintained at every step
• Ability to follow a VaR and P&L proxy/predict intraday
• Q&A on your specific use cases and environments

Key takeaways

• How to compute accurate, explainable risk metrics interactively on full-history data
• Why avoiding sampling/approximations improves decisions and governance
• Proof points on speed and scale for complex portfolios
• Practical paths to transparency for audit, controls, and stakeholder trust

Register here 👉