Towards Federated Graph Learning for Collaborative Financial Crimes Detection
IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）；IBM Research Almaden（IBM阿尔马登研究院）；IBM Research Tokyo（IBM东京研究院）；IBM Global Business Services（IBM全球企业咨询服务部）
Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel… 展开作者
Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran
Financial crime is a large and growing problem, in some way touching almost every financial institution. Financial institutions are the front line in the war against financial crime and accordingly, must devote substantial human and technology resources to this effort. Current processes to detect financial misconduct have limitations in their ability to effectively differentiate between malicious behavior and ordinary financial activity. These limitations tend to result in gross over-reporting of suspicious activity that necessitate time-intensive and costly manual review. Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms. Where financial institutions address financial crimes through the lens of their own firm, perpetrators may devise sophisticated strategies that may span across institutions and geographies. Financial institutions continue to work relentlessly to advance their capabilities, forming partnerships across institutions to share insights, patterns and capabilities. These public-private partnerships are subject to stringent regulatory and data privacy requirements, thereby making it difficult to rely on traditional technology solutions. In this paper, we propose a methodology to share key information across institutions by using a federated graph learning platform that enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrated that our federated model outperforms local model by 20% with the UK FCA TechSprint data set. This new platform opens up a door to efficiently detecting global money laundering activity.
HybridFL: A Federated Learning Approach for Financial Crime Detection
Department of Advanced Computing Sciences, Maastricht University（马斯特里赫特大学高级计算科学系）；Department of Cognitive Science and Artificial Intelligence, Tilburg University（蒂尔堡大学认知科学与人工智能系）
Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection
School of Computer Engineering, KIIT Deemed to be University（计算机工程学院，KIIT deemed to be 大学）；School of Electronics Engineering, KIIT Deemed to be University（电子工程学院，KIIT deemed to be 大学）
Quantum Cloud and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia（量子云与分布式系统实验室，计算机与信息系统学院，墨尔本大学，澳大利亚）；Department of Computer Science and Engineering, University of Visvesvaraya College of Engineering, Bangalore University, India（计算机科学与工程系，维萨瓦拉亚工程学院，班加罗尔大学，印度）
Artificial Intelligence-Enabled Accounting Information Systems and Fraud Detection in Nigeria's Financial Services Sector: The Moderating Role of Natural Language Processing
WeAreGenius Research Institute（WeAreGenius研究机构）；Park University（帕克大学）
