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Designing an attack-defense game: how to increase robustness of financial transaction models via a competition
We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input.
Hiding Backdoors within Event Sequence Data via Poisoning Attacks
This adoption brings new danger, as deep black-box models are known to be vulnerable to adversarial attacks.
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients.
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time.
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