Operationalising AI in capital markets risk infrastructures

 

 

Capital markets firms are pushing artificial intelligence beyond the pilot stage and into core risk and capital workflows, and the focus is shifting to what scalable deployment actually requires. Siloed architectures, data latency and limited explainability are the key challenges firms are working through, while the move from batch-based aggregation to intraday analytics is opening new possibilities for how infrastructure and governance are designed.

This webinar examines how firms are embedding AI into workflows such as the Fundamental Review of the Trading Book (FRTB) capital calculation, profit-and-loss (P&L) explain, and intraday risk monitoring. Also under discussion is how firms are keeping outputs transparent and auditable for internal decision-making and regulatory reporting, and how existing model risk and governance frameworks are evolving as large language models (LLMs) become embedded in critical infrastructure.

Key discussion points:

• How capital markets firms are moving AI initiatives beyond the pilot stage, and the role data latency, siloed architectures and limited explainability play in preventing scalable deployment
• How the shift from batch-based risk aggregation to intraday, decision-grade analytics is changing the infrastructure, data and governance requirements firms need to have in place
• How firms are embedding AI into core risk and capital workflows such as FRTB capital calculation, P&L explain and intraday risk monitoring, and what a front-to-back integrated approach looks like in practice
• How firms are ensuring that AI-driven outputs remain transparent and auditable for internal risk decision-making and regulatory reporting, and what meaningful explainability looks like when required to hold up under scrutiny
• How are firms approaching governance and model risk controls as AI and LLMs become embedded in core risk infrastructures, and where are existing frameworks proving insufficient?

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