  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.
