SR-Fraud：一种面向非平稳支付欺诈检测的结果监督反思式LLM智能体框架

中文摘要 AI 辅助

实时支付欺诈检测是一个非平稳的流式预测问题：对手在监督标签成熟之前就进行适应，局部突发攻击可能在重新训练之前就造成损失。生产系统通常依赖表格分类器和规则，这些方法在周期性重新训练之前往往难以捕捉新兴的序列模式。我们提出了SR-Fraud，一种结果监督的反思式LLM框架，将请求时决策与离线适应解耦。一个冻结的无状态智能体通过混合情景窗口对每笔交易进行评分，以跟踪行为变化，而一个离线反思智能体从已成熟的错误中提出边界假设。随后，一个确定性验证器仅将受支持的假设纳入可执行的知识状态。在生产支付欺诈基准上，SR-Fraud相比其冻结的决策智能体提升了所有检测指标，获得了比静态和周期性重新训练的CatBoost更高的点估计值，并检测到了新兴的欺诈突发。

英文摘要

Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized burst attacks can cause losses before retraining. Production systems typically rely on tabular classifiers and rules, which can struggle to capture these emerging sequential patterns before periodic retraining occurs. We present SR-Fraud, an outcome-supervised reflective LLM framework that decouples request-time decisions from offline adaptation. A frozen, stateless agent scores each transaction from a Hybrid Episodic Window to track behavioral shifts, while an offline reflection agent proposes boundary hypotheses from matured errors. A deterministic verifier then admits only supported hypotheses into an executable knowledge state. On a production payment-fraud benchmark, SR-Fraud improves all detection metrics over its frozen decision agent, obtains higher point estimates than static and periodically retrained CatBoost, and detects an emerging fraud burst.

发表机构

• Coinbase, Inc.（Coinbase公司）
• The Ohio State University（俄亥俄州立大学）

机构由 AI 辅助整理，请以论文原文为准。