15 citations · 18 across the 5 of their papers we have counts for
5 papers
SeqNAS: Neural Architecture Search for Event Sequence Classification
Igor Udovichenko, Egor Shvetsov, Denis Divitsky +6
Neural Architecture Search (NAS) methods are widely used in various industries to obtain high quality taskspecific solutions with minimal human intervention. Event Sequences find w…
Designing an attack-defense game: how to increase robustness of financial transaction models via a competition
Alexey Zaytsev, Maria Kovaleva, Alex Natekin +7
Banks routinely use neural networks to make decisions. While these models offer higher accuracy, they are susceptible to adversarial attacks, a risk often overlooked in the context…
Hiding Backdoors within Event Sequence Data via Poisoning Attacks
Alina Ermilova, Elizaveta Kovtun, Dmitry Berestnev +1
The financial industry relies on deep learning models for making important decisions. This adoption brings new danger, as deep black-box models are known to be vulnerable to advers…
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin +2
In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based…
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev +2
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. In this w…