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

arXiv 2308.11406 ↗First posted 22 Aug 2023cs.LG cs.CR q-fin.STcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 60 references that are in the map. First posted on arXiv 22 Aug 2023.

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26 of 60 references.

References · 26

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2013Intriguing properties of neural networkscited by 5,155
  4. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  5. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  6. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,451
  7. 2020Fast is better than free: Revisiting adversarial trainingcited by 727
  8. 2020RobustBench: a standardized adversarial robustness benchmarkcited by 661
  9. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  10. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 302
  11. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 237
  12. 2021Neural Temporal Point Processes: A Reviewcited by 114
14 more
  1. 2020Adversarial Training for Large Neural Language Modelscited by 111
  2. 2023Robust Evaluation of Diffusion-Based Adversarial Purificationcited by 65
  3. 2021Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Datacited by 45
  4. 2019Robust Decision Trees Against Adversarial Examplescited by 40
  5. 2020CoLES: Contrastive Learning for Event Sequences with Self-Supervisioncited by 32
  6. 2020Sharp Statistical Guarantees for Adversarially Robust Gaussian Classificationcited by 28
  7. 2019E.T.-RNN: Applying Deep Learning to Credit Loan Applicationscited by 26
  8. 2021Adversarial Attacks on Deep Models for Financial Transaction Recordscited by 24
  9. 2021Removing Adversarial Noise in Class Activation Feature Spacecited by 18
  10. 2022Improving Adversarial Robustness via Mutual Information Estimationcited by 13
  11. 2021Robust Optimal Classification Trees Against Adversarial Examplescited by 11
  12. 2020Adversarial Training Reduces Information and Improves Transferabilitycited by 10
  13. 2021Bank transactions embeddings help to uncover current macroeconomicscited by 3
  14. 2023Hiding Backdoors within Event Sequence Data via Poisoning Attackscited by 1

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