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

ArXiv ID: 2308.11406 “View on arXiv”

Authors: Unknown

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.

Keywords: Neural Networks, Adversarial Attacks, Financial Transactions, Fraud Detection, Banking

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper’s core contribution is a novel competition framework and an open dataset for testing model robustness, focusing on implementation and data rather than advanced mathematical theory. Empirical rigor is high due to the introduction of a new dataset, detailed competition results, and analysis of participant submissions under realistic adversarial conditions.

  flowchart TD
    A["Research Goal: How to improve robustness of<br>financial transaction models against adversarial attacks?"] --> B["Methodology: Direct Opposition Competition"]
    
    B --> C["Data Input:<br>Open Financial Transaction Dataset<br>(with credit default labels)"]
    
    C --> D["Process: Attackers & Defenders<br>compete in real-time"]
    
    D --> E["Computational Process:<br>Neural Network Training &<br>Adversarial Evaluation"]
    
    E --> F["Outcome 1: Novel Competition Structure<br>validated for financial ML"]
    
    E --> G["Outcome 2: Performance Analysis<br>of top attack/defense submissions"]
    
    E --> H["Outcome 3: Enhanced Robustness<br>framework for banking applications"]
    
    F --> I["Key Finding:<br>Direct opposition reveals<br>real-world vulnerabilities<br>in event sequence models"]