Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free)

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Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free)

This Article asks what happens next. The model has encoded its knowledge of fraud as symbolic rules. V14 below a threshold means fraud. What happens when that relationship starts to change?

Can the rules act as a canary? In other words: can neuro-symbolic concept drift monitoring work at inference time, without labels?

Full architecture background in Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules and How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment. You will follow this article without them, but the mechanism section makes more sense with context.

The post Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free) appeared first on Towards Data Science.

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