FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment

arXiv:2608.24551 · cs.LG, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/iHaydenzZ/FraudBench

Terminology

• Robustness — 73× in this paper · explained in 5 episodes
• AUC — 43× in this paper · explained in 2 episodes
• Benchmark — 36× in this paper · explained in 2 episodes
• Adversarial Robustness — 23× in this paper · explained in 3 episodes
• Adversarial Training — 10× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Adversarial Examples — 7× in this paper · explained in 2 episodes
• Benchmarking — 7× in this paper · explained in 4 episodes
• Filtered Success Rate — 6× in this paper
• Adversarial Attacks — 3× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Knowledge Discovery and Data Mining — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• International Conference on Learning Representations — 1× in this paper
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Explaining and Harnessing Adversarial Examples
• Fraud Dataset Benchmark and Applications
• Adaptive Stress Testing for Adversarial Learning in a Financial Environment
• Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

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