  Credit risk modeling has permeated our everyday life. Most banks and
financial companies use this technique to model their clients' trustworthiness.
While machine learning is increasingly used in this field, the resulting
large-scale collection of user private information has reinvigorated the
privacy debate, considering dozens of data breach incidents every year caused
by unauthorized hackers, and (potentially even more) information misuse/abuse
by authorized parties. To address those critical concerns, this paper proposes
a framework of Privacy-preserving Credit risk modeling based on Adversarial
Learning (PCAL). PCAL aims to mask the private information inside the original
dataset, while maintaining the important utility information for the target
prediction task performance, by (iteratively) weighing between a privacy-risk
loss and a utility-oriented loss. PCAL is compared against off-the-shelf
options in terms of both utility and privacy protection. Results indicate that
PCAL can learn an effective, privacy-free representation from user data,
providing a solid foundation towards privacy-preserving machine learning for
credit risk analysis.
