Interpretable Multimodal Learning for Intelligent Regulation in Online Payment Systems

TL;DR

This paper introduces a novel attention-based neural network model, CIAN, for multimodal data integration and interpretability in online payment regulation, demonstrating superior performance on real-world datasets.

Contribution

The paper proposes CIAN, a cross-modal and intra-modal attention network, with an interpretable explainer for financial regulation in online payments, addressing multimodal data use and model transparency.

Findings

CIAN outperforms state-of-the-art methods on real datasets.

The interpretability method effectively explains attention interactions.

The model enhances text-trade joint embedding learning.

Abstract

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.