ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models
[Sztuczna inteligencja] Autorzy: Ze Zhao, Haoyu Jiang, Weiyu Han i 4 więcej
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 …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja