{"type":"ScholarlyArticle","title":"Interpretable Multimodal Learning for Intelligent Regulation in Online Payment Systems","authors":[{"name":"Shuoyao Wang","slug":"shuoyao-wang","given":"Shuoyao","family":"Wang","affiliation":"College of Electronic and Information Engineering , Shenzhen University , China; Finan- cial Technology Group , Tencent , China."},{"name":"Diwei Zhu","slug":"diwei-zhu","given":"Diwei","family":"Zhu","affiliation":"Department of Information Engineering , The Chinese University of Hong Kong , Hong Kong"}],"abstract":"With the explosive growth of transaction activities in online payment systems, effective and real-time 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. \r\nOne main challenge of the AI-enabled regulation is how to  utilize multimedia information, i.e., multimodal signals, in Financial Technology (FinTech).\r\nInspired 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.\r\nMore specifically, we integrate the text and transaction information to enhance the text-trade joint-embedding learning, which clusters positive pairs and push negative  pairs  away  from  each  other.\r\nAnother 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.\r\nWith 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.","doi":"10.24963/ijcai.2020/645","url":"https://ijcai.org/proceedings/2020/645","pdf":"https://ijcai.org/proceedings/2020/0645.pdf","markdown":"https://ijcai.org/proceedings/2020/645.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/645","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":645,"pages":{"first":4675,"last":4681},"keywords":["Other areas: Interpretability of FinTech","AI for regulation: AI for financial crime detection","Foundation for AI in FinTech: Data mining and knowledge discovery for FinTech","AI for regulation: AI for international regulation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24072/file_24072.m3u8","length_sec":851,"provider":"hls"}],"references":[{"position":1,"raw":"[Afouras et al., 2018] T. 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