FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate because financial tabular data involve domain-speci...
arXiv:2609.09945v1 Announce Type: cross Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where ap...
arXiv:2608. 00566v1 Announce Type: new Abstract: Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and social welfare.
FinRED is an expert‑guided framework for generating and evaluating financial LLM red‑teaming tests. It maps global financial standards to specific threats, converts real documents into context‑rich prompts, and offers a validated rubric that reduces false negatives. The framework is deployed in South Korea’s Financial Security Institute sandbox and is gated for qualified researchers.
arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.