Understanding How Input-Output Relationships Change, How to Detect Them, and How to Adapt
9 min read22 hours ago
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When the Rules Change
Your fraud detection model was built on historical fraud patterns. It learned: “Unusual location + High amount = Fraud.”
This rule worked perfectly. For six months, the model caught 95% of fraud attempts.
Then fraudsters adapted.
They started using VPNs to mask their location. The “unusual location” signal disappeared. Your model suddenly catches only 60% of fraud attempts.
You didn’t change the model. You didn’t deploy new code. The rules the model l…
Understanding How Input-Output Relationships Change, How to Detect Them, and How to Adapt
9 min read22 hours ago
–
When the Rules Change
Your fraud detection model was built on historical fraud patterns. It learned: “Unusual location + High amount = Fraud.”
This rule worked perfectly. For six months, the model caught 95% of fraud attempts.
Then fraudsters adapted.
They started using VPNs to mask their location. The “unusual location” signal disappeared. Your model suddenly catches only 60% of fraud attempts.
You didn’t change the model. You didn’t deploy new code. The rules the model learned are still technically correct — they’re just no longer predictive of fraud.
This is concept drift.
Concept drift refers to changes in the statistical relations between input data and target values, meaning the relationship between input features and output labels fundamentally changes over time. Unlike data drift, which concerns changes in input distributions, concept drift concerns the predictive relationship becoming invalid.
The critical insight: When the world changes, the rules must change too — or the model becomes a liability.