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Algorithms are increasingly common components of high-impact decision-making,and a growing body of literature on adversarial examples in laboratory settingsindicates that standard machine learning models are not robust. This suggeststhat real-world systems are also susceptible to manipulation ormisclassification, which especially poses a challenge to machine learningmodels used in financial services. We use the loan grade classification problemto explore how machine learning models are sensitive to small changes inuser-reported data, using adversarial attacks documented in the literature andan original, domain-specific attack. Our work shows that a robust optimizationalgorithm can build models for financial services that are resistant tomisclassification on perturbations. To the best of our knowledge, this is thefirst study of adversarial attacks and defenses for deep learning in financialservices.

Robust Classification of Financial Risk