Learn · Fraud Threshold
Fraud detection: the threshold trade-off
A fraud model gives each transaction a risk score. You still have to pick the line at which you block it. Slide that line and watch the trade-off every fincrime team lives with: catch more fraud and you flood analysts with false alarms; cut the false alarms and you let real fraud through. The maths below is real — only the distributions are illustrative.
Illustrative distributions. Because fraud is rare, its true curve (magenta) is tiny next to the legitimate one — that is class imbalance. The dashed gold outline shows the fraud curve's shape magnified so you can see where it sits.
Block any transaction scoring at or above this line.
Share of transactions that are actually fraud (0.5%–10%).
At this threshold
Confusion matrix
Out of an illustrative population of 10,000 transactions (200 actually fraud).
The honest caveat: real transaction scores are never two neat bell curves, fraud patterns drift, and the "right" threshold depends on the cost of a missed case versus a blocked customer — a business and ethics decision, not just a maths one. That is exactly the kind of responsible-AI trade-off we work through with fincrime teams at bigspark. New to precision and recall? See the Learn shelf.