Investment analytics and data services
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Against a backdrop of heightened market volatility, liquidity has moved from being an operational requirement to a strategic priority. In this discussion, Rémi and Thomas explain how BNP Paribas’ Securities Services business is helping institutional investors bridge the gap between analytics and collateral allocation, enabling predictive, data-enabled collateral management.
Hear from our experts
How can analytics support collateral optimisation?
Collateral management was once largely viewed as an operational burden. But market dynamics have changed. Rémi highlights how regulatory reforms have increased margin requirements, while recent volatility in interest rates and foreign exchange markets has made liquidity and margin risks far more visible. For institutional investors, this raises a critical question: how can they meet future margin calls under stressed market conditions without disrupting their investment strategy?
Rémi outlines how our Securities Services business is addressing this challenge with its Predictive Collateral Coverage Reporting (PCCR) solution designed to turn fragmented information into actionable insights.
Thomas explains that this solution is built on the combination of deep collateral expertise and a modern data and analytics infrastructure. This enables clients to move beyond simply managing collateral and towards actively optimising capital, liquidity, and risk. He highlights three core technical components that underpin this approach: data aggregation, the implementation of rigorous controls and processing power.
This allows experts to move beyond operational processing, providing clients with enhanced visibility and a more efficient capital utilisation, Thomas adds
This is complemented by continuous investments in our enterprise data capabilities. The development of our Data Factory solution, a business-wide data repository, has notably been a core enabler for our predictive collateral coverage solution. It combines data warehouse aspects with an advanced three-pillar data qualification framework that incorporates our different data quality dimensions. This supports our Securities Services business in acting as a trusted data and analytics provider for our clients.
The next step for collateral allocation
For Rémi, predictive collateral management needs to become increasingly forward-looking, interactive, and embedded in decision-making. He explains that the predictive collateral solution already enables institutional investors to forecast collateral needs across counterparties and portfolios, using stressed test scenarios, CSA rules and long-term horizons.
Going forward, the ambition is to further strengthen these predictive capabilities.
In today’s environment, the goal is not simply to understand the present, but to anticipate the future, Rémi states.
Agentic AI to support anticipation
One theme shaping the next stage of data and analytics is agentic AI. Unlike traditional analytics, AI agents can be outcome-driven, access data across multiple domains, and proactively recommend solutions.
Thomas explains that the shift is from automation to anticipation: helping detect risks and simulate scenarios before issues materialise. In addition, by handling repetitive analysis and data gathering, AI will allow teams to focus on high-value judgment and validation.
