Credit scoring systems are critical FinTech applications that concern the analysis of the creditworthiness of a person or organization. While decisions were previously based on human expertise, they are now increasingly relying on data analysis and machine learning. In this paper, we assess the ability of state-of-the-art adversarial machine learning to craft attacks on a real-world credit scoring system. Interestingly, we find that, while these techniques can generate large numbers of adversarial data, these are practically useless as they all violate domain-specific constraints. In other words, the generated examples are all false positives as they cannot occur in practice. To circumvent this limitation, we propose CoEvA2, a search-based method that generates valid adversarial examples (satisfying the domain constraints). CoEvA2 utilizes multi-objective search in order to simultaneously handle constraints, perform the attack and maximize the overdraft amount requested. We evaluate CoEvA2 on a major bank's real-world system by checking its ability to craft valid attacks. CoEvA2 generates thousands of valid adversarial examples, revealing a high risk for the banking system. Fortunately, by improving the system through adversarial training (based on the produced examples), we increase its robustness and make our attack fail.

Transferability is the property of adversarial examples to be misclassified by other models than the surrogate model for which they were crafted. Previous research has shown that transferability is substantially increased when the training of the surrogate model has been early stopped. A common hypothesis to explain this is that the later training epochs are when models learn the non-robust features that adversarial attacks exploit. Hence, an early stopped model is more robust (hence, a better surrogate) than fully trained models.We demonstrate that the reasons why early stopping improves transferability lie in the side effects it has on the learning dynamics of the model. We first show that early stopping benefits transferability even on models learning from data with non-robust features. We then establish links between transferability and the exploration of the loss landscape in the parameter space, on which early stopping has an inherent effect. More precisely, we observe that transferability peaks when the learning rate decays, which is also the time at which the sharpness of the loss significantly drops.This leads us to propose RFN, a new approach for transferability that minimizes loss sharpness during training in order to maximize transferability. We show that by searching for large flat neighborhoods, RFN always improves over early stopping (by up to 47 points of transferability rate) and is competitive to (if not better than) strong state-of-theart baselines.

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