arXiv cs.AI· Ze Zhao, Haoyu Jiang, Weiyu Han, Ruobing Zuo, Xiao Tan, Shuai Chen, Tianyi Zhang·· 1 天前AI 评分27
ID2Pattern：为交易风险基础模型预训练可泛化的身份行为模式
ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models
AI 导读
ID2Pattern 是一个面向交易风险基础模型的预训练框架，通过 Local Anonymous ID（LAID）tokenizer 编码卡号、设备、账户等高基数身份字段，再用多尺度模式码本将 LAID 序列抽象为离散行为模式。在超 1 亿条序列的工业数据集和六个公开数据集上，该方法平均分别提升 4.13% 和 8.83%，且在训练中所有身份值均未见过时依然有效。
正文 · 原文
Abstract:Large-scale behavioral pretraining offers a promising route to generalizable representations in online payment systems. However, high-cardinality identity fields such as card, device, and account IDs, though rich in behavioral signals, remain underexploited. Their continually growing vocabularies and limited semantic meaning make existing vocabulary-based and semantics-based encodings ill-suited. Their generalizable signal lies not in specific values, but in the identity behavior patterns they exhibit across a transaction sequence. Building on this insight, we propose ID2Pattern, a pretraining framework that brings these patterns into transaction foundation models. ID2Pattern first encodes identity fields with a Local Anonymous ID (LAID) tokenizer, which captures how each identity appears and recurs within the sequence, independent of its specific value. It then pretrains a multi-scale pattern codebook on unlabeled sequences, which abstracts LAID sequences into discrete behavior patterns for downstream prediction. Experiments on an industrial dataset with over 100M sequences and six public datasets show that ID2Pattern consistently outperforms strong baselines, improving industrial by 4.13% and public datasets by 8.83% on average. Notably, ID2Pattern remains equally effective even when all identity values are unseen during training, highlighting its potential to generalize to new users and markets.
| Comments: | 13 pages, 6 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.41988 [cs.AI] |
| (or arXiv:2610.41988v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41988 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ze Zhao [view email]
[v1]
Fri, 30 Oct 2026 16:35:43 UTC (1,967 KB)
来源：arXiv cs.AI · arxiv.org