Designing an attack-defense game: how to increase robustness of financial transaction models via a competition
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910611509608448 |
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| author | Zaytsev, Alexey Kovaleva, Maria Natekin, Alex Vorsin, Evgeni Smirnov, Valerii Smirnov, Georgii Sidorshin, Oleg Senin, Alexander Dudin, Alexander Berestnev, Dmitry |
| author_facet | Zaytsev, Alexey Kovaleva, Maria Natekin, Alex Vorsin, Evgeni Smirnov, Valerii Smirnov, Georgii Sidorshin, Oleg Senin, Alexander Dudin, Alexander Berestnev, Dmitry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_11406 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Designing an attack-defense game: how to increase robustness of financial transaction models via a competition Zaytsev, Alexey Kovaleva, Maria Natekin, Alex Vorsin, Evgeni Smirnov, Valerii Smirnov, Georgii Sidorshin, Oleg Senin, Alexander Dudin, Alexander Berestnev, Dmitry Machine Learning Cryptography and Security Statistical Finance 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. |
| title | Designing an attack-defense game: how to increase robustness of financial transaction models via a competition |
| topic | Machine Learning Cryptography and Security Statistical Finance |
| url | https://arxiv.org/abs/2308.11406 |