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arXiv · 2308.11406

Designing an attack-defense game: how to increase robustness of financial transaction models via a competition

Alexey Zaytsev·Maria Kovaleva·Alex Natekin·Evgeni Vorsin·Valerii Smirnov·Georgii Smirnov·Oleg Sidorshin·Alexander Senin·Alexander Dudin·Dmitry Berestnev
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Abstract

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 of event sequences, particularly sequences of financial transactions, as most works consider computer vision and NLP modalities. We propose a thorough approach to studying these risks: a novel type of competition that allows a realistic and detailed investigation of problems in financial transaction data. The participants directly oppose each other, proposing attacks and defenses -- so they are examined in close-to-real-life conditions. The paper outlines our unique competition structure with direct opposition of participants, presents results for several different top submissions, and analyzes the competition results. We also introduce a new open dataset featuring financial transactions with credit default labels, enhancing the scope for practical research and development.

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Alexey Zaytsev, Maria Kovaleva, Alex Natekin, Evgeni Vorsin, Valerii Smirnov, Georgii Smirnov, Oleg Sidorshin, Alexander Senin, Alexander Dudin, Dmitry Berestnev. 2023-08-22. Designing an attack-defense game: how to increase robustness of financial transaction models via a competition. https://arxiv.org/abs/2308.11406

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