Robust Classification of Financial Risk
This addresses the risk of manipulation in high-impact financial decisions, but it is incremental as it applies known adversarial defense methods to a new domain.
The paper tackled the problem of machine learning models being vulnerable to adversarial attacks in financial services, specifically for loan grade classification, and showed that a robust optimization algorithm can build models resistant to misclassification on perturbations.
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.