用自注意力机制处理银行交易流，提升分类与风控效果。
Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow
- 将自注意力引入多模态银行交易数据处理，设计专用分词方法。
- 预训练模型微调后，在分类与信用风险任务上超越现有最佳方法。
- 适合金融风控、交易分析领域的研究者与工程师参考。
银行交易流（BTF）是营销、信用风险或金融欺诈等银行业务中常见的序列数据，由日期、数值和文本三类模态构成。本文提出将自注意力机制应用于BTF处理，基于大量BTF数据以自监督方式训练了两种通用模型：一种基于RNN，另一种基于Transformer。为处理BTF，我们设计了特定的分词策略。在两个下游任务——交易分类与信用风险评估上对模型性能进行了评估。结果表明，对这两个预训练模型进行微调后，均在两项任务上表现优于当前最优方法。
原文摘要 · Abstract (English)
Banking Transaction Flow (BTF) is a sequential data found in a number of banking activities such as marketing, credit risk or banking fraud. It is a multimodal data composed of three modalities: a date, a numerical value and a wording. We propose in this work an application of self-attention mechanism to the processing of BTFs. We trained two general models on a large amount of BTFs in a self-supervised way: one RNN-based model and one Transformer-based model. We proposed a specific tokenization in order to be able to process BTFs. The performance of these two models was evaluated on two banking downstream tasks: a transaction categorization task and a credit risk task. The results show that fine-tuning these two pre-trained models allowed to perform better than the state-of-the-art approaches for both tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。