Jamie Hutton, CTO at Quantexa, explains how decision intelligence uses entity resolution to merge messy data into a unified graph. This approach has doubled their model accuracy and nearly eliminated AI hallucinations. For regulated industries like banking and insurance, explainability matters more than black-box deep learning.
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Anyone who says you can totally eliminate hallucinations is unfortunately— it's not really possible, but you can minimize them to an extent where they're really a non-issue. Decisions made on context. That's what we really mean when we say decision intelligence, really resolving those down. So you've got one single view of the truth. That's the first step in the process. We call it entity resolution.
A big selling point of data analysis has been data-driven decision making, where you use data to make better decisions than gut feel. Feeling alone. The next evolution is decision intelligence, where you optimize and automate decision-making. It's a fascinating field combining data science and AI with a lot of worrying about data quality and data silos and a smattering of process improvement.
So what we had to do was design the system from the ground up to work in that space of messy, disparate, and even intentionally manipulated data. And they've doubled the accuracy of their underlying decisioning models just by feeding in better context.
I want to find out what's possible with decision intelligence and how to achieve it. Our guest is Jamie Hutton, the CTO at decision intelligence company Quantexa. Jamie's in charge of building the decision intelligence platform, and the focus is on fighting financial crime and enabling regulatory compliance for banking, insurance, and governments.
Deep learning, great, but black box. You cannot use it in compliance and regulated use cases because you have to explain how the model works. So you can essentially turbocharge the LLMs by providing them with a graph-based context. Let's find out how to make intelligent decisions.
