Regulatory compliance demands pertaining to banking book risks are increasing, notably with the introduction of EBA rules for the measurement of IRRBB and the forthcoming EBA reporting. Banks of all sizes now need to provide regulatory submissions with detailed and validated numbers on a timely basis.
By combining the strengths of SS&C Algorithmics and Regnology, we’re providing banks with a simplified data ingestion process for a seamless end-to-end risk and regulatory solution. Our shared capabilities span from powerful calculations to streamlined workflows for regulatory report creation and submission.
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The Data Point Model (DPM) 2.1 is creating a unified supervisory data ecosystem in the EU by establishing a machine-readable and legally traceable metadata environment. This evolution is designed to improve data consistency, enhance traceability, and unlock powerful AI use cases for automated impact analysis and regulatory change management. The consultation is open till 30 September 2026.
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Financial supervisory authorities are increasingly leveraging cloud-native infrastructure, granular data, and AI-enhanced oversight to transition toward a more data-driven, and forward-looking approach to supervision.
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As regulatory pressures grow, risk, finance, and reporting must evolve from fragmented data silos to an intelligent, integrated value chain. Our new whitepaper with Chartis outlines the path to building a more efficient & resilient regulatory operating model.
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