  The success of deep learning in recent years have led to a significant
increase in interest and prevalence for its adoption to tackle financial
services tasks. One particular question that often arises as a barrier to
adopting deep learning for financial services is whether the developed
financial deep learning models are fair in their predictions, particularly in
light of strong governance and regulatory compliance requirements in the
financial services industry. A fundamental aspect of fairness that has not been
explored in financial deep learning is the concept of trust, whose variations
may point to an egocentric view of fairness and thus provide insights into the
fairness of models. In this study we explore the feasibility and utility of a
multi-scale trust quantification strategy to gain insights into the fairness of
a financial deep learning model, particularly under different scenarios at
different scales. More specifically, we conduct multi-scale trust
quantification on a deep neural network for the purpose of credit card default
prediction to study: 1) the overall trustworthiness of the model 2) the trust
level under all possible prediction-truth relationships, 3) the trust level
across the spectrum of possible predictions, 4) the trust level across
different demographic groups (e.g., age, gender, and education), and 5)
distribution of overall trust for an individual prediction scenario. The
insights for this proof-of-concept study demonstrate that such a multi-scale
trust quantification strategy may be helpful for data scientists and regulators
in financial services as part of the verification and certification of
financial deep learning solutions to gain insights into fairness and trust of
these solutions.
