Project Summary

Financial operational risks cost the financial sector billions annually due to fragmented systems, operations, data and knowledge silos. As financial institutions face increasing complexity, cyber threats, and regulatory scrutiny, the need for dynamic, real-time risk modelling has never been more urgent.

IRMAI is a visionary start-up for AI-driven risk management, which is actively working on tackling these challenges via proactive risk automation, contextual intelligence, and compliance oversight. To realise its vision at scale, IRMAI is currently building an innovative AI-driven digital twin of financial operations to support risk management in dynamic financial ecosystems. However, a significant bottleneck actively slowing down IRMAI’s progress is the lack of a robust and trustworthy digital data infrastructure that their AI can rely on.

In a reply to this urgency and informed by scientific literature, this project will investigate the utilisation of a multimodal knowledge graph (MMKG) as digital infrastructure to enable greater interoperability and efficient integration of financial operations data. The results of this will not only boost IRMAI’s developments but will also help to better understand the advantages and disadvantages of utilising a MMKG as a digital twin infrastructure for contextualising and operationalising financial operations risks.

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