Wells touts new explainability technique for AI credit models

Novel interpretability method could spur greater use of ReLU neural networks for credit scoring

Neural networks

A team of researchers at Wells Fargo has begun deploying a novel explainability technique for deep learning models – something the bank hopes will allow it to begin using more complex approaches to power credit decisioning.

Banks have long sought to tap the potential of neural networking – a family of deep learning approaches that works by seeking to replicate human thought patterns – for complex problem-solving in credit risk. Yet the technique finds itself underused, since models that rest on

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