论文标题
在线支付系统中的智能法规的可解释的多模式学习
Interpretable Multimodal Learning for Intelligent Regulation in Online Payment Systems
论文作者
论文摘要
随着在线支付系统中交易活动的爆炸性增长，有效和实时法规成为付款服务提供商的关键问题。得益于人工智能（AI）的快速发展，AI-ni-Anable法规成为有前途的解决方案。支持AI的法规的一个主要挑战是如何利用多媒体信息，即多模式信号，用于金融技术（金融科技）。受自然语言处理中的注意机制的启发，我们提出了一种新型的跨模式和模式内注意网络（CIAN），以研究文本与交易之间的关系。更具体地说，我们整合了文本和交易信息，以增强文本贸易联合学习学习，从而将正对并将负面对脱离彼此。智能监管的另一个挑战是复杂的机器学习模型的解释性。为了维持金融监管的要求，我们设计了一个CIAN解释器，以解释注意力机制如何相互作用，该特征被称为低级矩阵近似问题。通过最大的在线支付系统的真实数据集，Tencent的微信工资，我们进行了实验以验证CIAN的实际应用值，我们的方法在其中优于最先进的方法。
With the explosive growth of transaction activities in online payment systems, effective and realtime regulation becomes a critical problem for payment service providers. Thanks to the rapid development of artificial intelligence (AI), AI-enable regulation emerges as a promising solution. One main challenge of the AI-enabled regulation is how to utilize multimedia information, i.e., multimodal signals, in Financial Technology (FinTech). Inspired by the attention mechanism in nature language processing, we propose a novel cross-modal and intra-modal attention network (CIAN) to investigate the relation between the text and transaction. More specifically, we integrate the text and transaction information to enhance the text-trade jointembedding learning, which clusters positive pairs and push negative pairs away from each other. Another challenge of intelligent regulation is the interpretability of complicated machine learning models. To sustain the requirements of financial regulation, we design a CIAN-Explainer to interpret how the attention mechanism interacts the original features, which is formulated as a low-rank matrix approximation problem. With the real datasets from the largest online payment system, WeChat Pay of Tencent, we conduct experiments to validate the practical application value of CIAN, where our method outperforms the state-of-the-art methods.
