TECH & AI
What do you see as the most promising use cases for LLMs in finance beyond customer service and document processing?
Simon Thompson, Head of AI, ML and Data Science, GFT LLMs are text-based generative AIs, and I think that’ s the clue. Where generating text is going to be helpful, that’ s where the promising use cases are.
For example, creating credit memos or underwriting reports can be tedious and repetitive and LLMs can supercharge employees doing this.
Of course, careful thought has to be applied to preventing crazy AI-slop text from being fired into critical processes.
Step-by-step generation requiring employee interaction can produce a user experience that is more like piloting an excavator rather than either digging trenches by hand or watching a robot build something.
“Careful thought has to be applied to preventing crazy AI-slop text from being fired into critical processes”
SIMON THOMPSON, HEAD OF AI, ML AND DATA SCIENCE, GFT
Richard Doherty, Wealth & Asset Management Leader at Publicis Sapient Beyond customer service and document processing, LLMs show exceptional promise in areas like real-time risk monitoring, personalised investment advisory and regulatory change management.
For instance, using LLMs to scan, summarise and contextualise global regulatory changes can give compliance teams a real-time edge, turning a reactive function into a proactive capability.
Similarly, AI-driven decision support for portfolio managers, based on massive cross-market and news analysis, has the potential to fundamentally reshape investment workflows.
Real-time risk monitoring is another game-changer. LLMs can process vast streams of market data, news, and regulatory communications to identify emerging risks before they show up in traditional metrics. This transforms risk management from reactive monitoring to predictive intelligence.
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