# Preprint reports improved payment fraud detection without added inference cost

_Published Monday, November 9, 2026 at 7:06 AM EST · Science, AI · Latest · Tier 2 — Notable_

Researchers report in a preprint that a payment fraud policy improved dollar-weighted precision without adding inference cost. On a held-out four-month test, it beat a prior agent trained only on labeled transactions by +4.7, +2.4 and +3.0 points at 80%, 85% and 90% recall, respectively.

The method uses a language model to write a reward function that evaluates both labeled transactions and previously blocked transactions lacking fraud labels. For the latter, it contrasts a newer risk signal with the scores behind the original blocks. The resulting policy retained the prior agent's architecture and beat the production baseline by +6.8 points on average; the reported results come from a held-out test.

## Sources

- [cs.CL updates on arXiv.org](https://arxiv.org/abs/2611.08706)

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Canonical: https://techandbusiness.org/newswire/__-vZf7NA_nE0mI_9_6-D-
Published: 2026-11-09T12:06:12.834Z
Story chronology: 2026-11-09T05:00:00.000Z
Retrieved: 2026-11-09T14:07:57.033Z
Publisher: Tech & Business (techandbusiness.org)