Waters Wrap: The path to generative AI is paved with solid data practices

While large language models are likely to proliferate, those that can develop a solid data infrastructure of taxonomies, ontologies, data sourcing, mapping and lineage will be the ultimate winners, Anthony says.

Credit: Adolphe Appian

In the highly regulated capital markets, there’s a dichotomy between things you “want to do” and things you “need to do”. Banks and asset managers want to deploy high-powered, machine-learning-driven analytics tools, but they need to solve ever-greater regulatory reporting needs. But does it have to be this way? Is there a way to bridge the gap?

(I hope you’ll follow along with me as this is a bit of a think piece. And I’d love feedback to see how I can improve this theory.)

First, let’s look

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The IMD Wrap: Will banks spend more on AI than on market data?

As spend on generative AI tools exceeds previous expectations, Max showcases one new tool harnessing AI to help risk and portfolio managers better understand data about their investments—while leaving them always in control of any resulting decisions.

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