用时间感知的GPT模型提升支付欺诈检测精度
FraudTransformer: Time-Aware GPT for Transaction Fraud Detection
- 在GPT架构中加入时间编码与可学习位置编码，捕捉交易顺序和时间间隔
- 在千万级真实交易数据上，AUROC和PRAUC均优于传统模型与基线Transformer
- 适合需要高时效性欺诈识别的金融风控系统使用
实时银行交易中的支付欺诈检测需依赖能利用事件顺序及不规则时间间隔的模型。我们提出 FraudTransformer，一种在标准 GPT 架构基础上增强的序列模型：(i) 引入专用时间编码器，对绝对时间戳或事件间间隔进行嵌入；(ii) 使用可学习的位置编码器以保留相对顺序。在包含数千万笔交易与辅助事件的大规模工业数据集上实验表明，FraudTransformer 超越四种强基线模型（逻辑回归、XGBoost、LightGBM）以及缺少时间或位置组件的 Transformer 变体。在留出测试集上，其达到最高的 AUROC 与 PRAUC。
原文摘要 · Abstract (English)
Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransformer, a sequence model that augments a vanilla GPT-style architecture with (i) a dedicated time encoder that embeds either absolute timestamps or inter-event values, and (ii) a learned positional encoder that preserves relative order. Experiments on a large industrial dataset -- tens of millions of transactions and auxiliary events -- show that FraudTransformer surpasses four strong classical baselines (Logistic Regression, XGBoost and LightGBM) as well as transformer ablations that omit either the time or positional component. On the held-out test set it delivers the highest AUROC and PRAUC.
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