# Robust Classification of Financial Risk

arXiv: https://arxiv.org/abs/1811.11079 · HTML: https://mlsanity.com/paper/1811.11079/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models](https://mlsanity.com/paper/2406.05491/) (2024)
- [CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks](https://mlsanity.com/paper/2407.10179/) (2024)
- [Feature Importance Guided Attack: A Model Agnostic Adversarial Attack](https://mlsanity.com/paper/2106.14815/) (2021)
- [When and How to Fool Explainable Models (and Humans) with Adversarial Examples](https://mlsanity.com/paper/2107.01943/) (2021)
- [RAGUEL: Recourse-Aware Group Unfairness Elimination](https://mlsanity.com/paper/2208.14175/) (2022)
- [MISLEAD: Manipulating Importance of Selected features for Learning Epsilon in Evasion Attack Deception](https://mlsanity.com/paper/2404.15656/) (2024)
- [Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP](https://mlsanity.com/paper/2502.19269/) (2025)
- [Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending](https://mlsanity.com/paper/2609.09945/) (2026)

## Abstract (arXiv)

Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, which especially poses a challenge to machine learning models used in financial services. We use the loan grade classification problem to explore how machine learning models are sensitive to small changes in user-reported data, using adversarial attacks documented in the literature and an original, domain-specific attack. Our work shows that a robust optimization algorithm can build models for financial services that are resistant to misclassification on perturbations. To the best of our knowledge, this is the first study of adversarial attacks and defenses for deep learning in financial services.