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Financial crime is a large and growing problem, in some way touching almost every financial institution.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé M Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 1902
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Distributed edge partitioning for trillion-edge graphs
Masatoshi Hanai, Toyotaro Suzumura, Wen Jun Tan, Elvis S. Liu, Georgios Theodoropoulos, and Wentong Cai · 1908
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Graph based anomaly detection and description: A survey
Leman Akoglu, Hanghang Tong, and Danai Koutra · 2015
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Graphsc: Parallel secure computation made easy
K. Nayak, X. S. Wang, S. Ioannidis, U. Weinsberg, N. Taft, and E. Shi · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Using social network analysis to prevent money laundering
Andrea Fronzetti Colladon and Elisa Remondi · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Graph analytics for real-time scoring of cross-channel transactional fraud
Ian Molloy, Suresh Chari, Ulrich Finkler, Mark Wiggerman, Coen Jonker, Ted Habeck, Youngja Park, Frank Jordens, and Ron Schaik · 2016
Cited alongside, same era.
Detection of money laundering groups using supervised learning in networks
David Savage, Qingmai Wang, Pauline Lienhua Chou, Xiuzhen Zhang, and Xinghuo Yu · 2016
Cited alongside, same era.
Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2017
Cited alongside, same era.
Efficient breadth-first search on massively parallel and distributed-memory machines
Koji Ueno, Toyotaro Suzumura, Naoya Maruyama, Katsuki Fujisawa, and Satoshi Matsuoka · 2017
Cited alongside, same era.
A multi-agent system based approach to fight financial fraud: An application to money laundering
Claudio Alexandre · 2018
Cited alongside, same era.
Nextgen aml: Distributed deep learning based language technologies to augment anti money laundering investigation
Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, and Dadong Wan · 2018
Later among the works it cites.
A hybrid approach to privacy-preserving federated learning, 12 2018
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, and Rui Zhang · 2018
Later among the works it cites.
Scalable graph learning for anti-money laundering: A first look
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, and Tao B. Schardl · 2018
Later among the works it cites.
In https://www.fca.org.uk/events/techsprints/2019-global-aml-and-financial-crime-techsprint , 2019
2019 global aml and financial crime techsprint · 2019
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A novel multiobjective approach for detecting money laundering with a neuro-fuzzy technique
M. B. Jamshidi, M. Gorjiankhanzad, A. Lalbakhsh, and S. Roshani · 2019
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Machine learning techniques for anti-money laundering (aml) solutions in suspicious transaction detection: a review
Zhiyuan Chen, Le Dinh Van Khoa, Ee Na Teoh, Amril Nazir, Ettikan Karuppiah, and Kim Sim Lam · 2018
Cited alongside, same era.
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A scalable attribute-aware network embedding system
Weiyi Liu, Zhining Liu, Fucai Yu, Pin-Yu Chen, Toyotaro Suzumura, and Guangmin Hu · 2019
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