论文标题
交易监控中的机器学习：XAI的前景
Machine Learning in Transaction Monitoring: The Prospect of xAI
论文作者
论文摘要
银行承担社会责任和监管要求，以减轻金融犯罪的风险。风险缓解主要是通过通过交易监控（TM）监视客户活动来进行的。最近，已经提出了机器学习（ML）来确定可疑的客户行为，这对ML模型及其产出的信任和解释性提高了社会技术的复杂含义。但是，由于其敏感性，几乎没有研究。我们旨在通过提出实证研究来填补这一差距，探讨ML支持自动化和增强如何影响TM流程以及利益相关者对建立可解释的人工智能（XAI）的要求。我们的研究发现，XAI的要求取决于TM过程中承担责任方的要求，该方面的变化取决于TM的增强或自动化。背景权的解释可以为审计提供急需的支持，并可能减少调查人员判断的偏见。这些结果表明，XAI的用例特异性方法可以充分促进ML在TM中的采用。
Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.
