使用机器学习检测金融交易描述中的滥用
Detection of Abuse in Financial Transaction Descriptions Using Machine Learning
摘要：
自从对新的支付平台（NPP）进行修改，将更长的信息作为支付说明以来，人们已经确定人们现在正在使用它进行通信，在某些情况下，该系统被用作家庭暴力的目标形式。这类技术辅助的滥用行为在识别、行动和纠正这种行为的方法方面提出了新的挑战。澳大利亚联邦银行人工智能实验室团队（CBA AI Labs）开发了一个新系统，该系统利用自然语言处理（NLP）深度学习模型的进步来创建一个强大的滥用检测器，定期对所有交易进行评分，并在数百万条记录中识别高风险滥用案例。在本文中，我们描述了银行服务背景下的技术辅助滥用问题，概述了开发的模型及其性能，以及更广泛的操作框架。
英文摘要：
Since introducing changes to the New Payments Platform (NPP) to include longer messages as payment descriptions, it has been identified that people are now using it for communication, and in some cases, the system was being used as a targeted form of domestic and family violence. This type of tech-assisted abuse poses new challenges in terms of identification, actions and approaches to rectify this behaviour. Commonwealth Bank of Australia's Artificial Intelligence Labs team (CBA AI Labs) has developed a new system using advances in deep learning models for natural language processing (NLP) to create a powerful abuse detector that periodically scores all the transactions, and identifies cases of high-risk abuse in millions of records. In this paper, we describe the problem of tech-assisted abuse in the context of banking services, outline the developed model and its performance, and the operating framework more broadly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
