  Bias in data can have unintended consequences that propagate to the design,
development, and deployment of machine learning models. In the financial
services sector, this can result in discrimination from certain financial
instruments and services. At the same time, data privacy is of paramount
importance, and recent data breaches have seen reputational damage for large
institutions. Presented in this paper is a trusted model-lifecycle management
platform that attempts to ensure consumer data protection, anonymization, and
fairness. Specifically, we examine how datasets can be reproduced using deep
learning techniques to effectively retain important statistical features in
datasets whilst simultaneously protecting data privacy and enabling safe and
secure sharing of sensitive personal information beyond the current
state-of-practice.
