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The paper introduces SeqLLM, a framework that enhances large language models (LLMs) by integrating behavioral-sequence modeling to improve merchant risk control at WeChat Pay. By employing a compact discrete vocabulary for behavioral events, a lightweight projector for semantic grounding, and prefix-guided capability injection for task-specific fine-tuning, SeqLLM significantly boosts screening precision from 92.0% to 97.5% compared to the existing DeepSeek-based LLM. Additionally, SeqLLM demonstrates superior performance on public recommendation benchmarks, achieving up to 32% relative improvement in Recall@5 while maintaining strong language capabilities.
Screening precision for merchant risk control at WeChat Pay skyrockets from 92.0% to 97.5% with SeqLLM's innovative integration of behavioral-sequence modeling.
Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful activity undetected. The hardest cases require jointly understanding a merchant's textual profile and long behavioral sequence. Large language models (LLMs) excel at text but cannot natively model such sequences, while adapting them often causes catastrophic forgetting. We present SeqLLM, a framework that adds behavioral-sequence modeling to a pretrained LLM while preserving its language ability. SeqLLM combines three components: a compact discrete vocabulary that represents behavioral events as native tokens; a lightweight projector, trained with a two-stage alignment curriculum, that grounds these tokens in the LLM's semantic space; and prefix-guided capability injection, which acquires sequence-modeling ability through task-prefixed supervised fine-tuning rather than continual pre-training. SeqLLM is deployed at WeChat Pay, screening millions of merchants daily. Against the production DeepSeek-based LLM baseline, it raises screening precision from 92.0% to 97.5%. Its pretrained behavior-token embeddings also improve Precision@Top-0.01% by 26.8 percentage points in a production fraud detector serving billion-scale transaction traffic. Beyond payments, SeqLLM achieves state-of-the-art results on public recommendation benchmarks. On MovieLens and Amazon, it surpasses the strong User-LLM baseline by up to 32% relative Recall@5 while retaining markedly stronger language ability. On RecIF, it improves Pass@32 by 14.2% over the full OneRec-8B pipeline using only one-fifth of its GPU-days.
