  Algorithmic fairness in lending today relies on group fairness metrics for
monitoring statistical parity across protected groups. This approach is
vulnerable to subgroup discrimination by proxy, carrying significant risks of
legal and reputational damage for lenders and blatantly unfair outcomes for
borrowers. Practical challenges arise from the many possible combinations and
subsets of protected groups. We motivate this problem against the backdrop of
historical and residual racism in the United States polluting all available
training data and raising public sensitivity to algorithimic bias. We review
the current regulatory compliance protocols for fairness in lending and discuss
their limitations relative to the contributions state-of-the-art fairness
methods may afford. We propose a solution for addressing subgroup
discrimination, while adhering to existing group fairness requirements, from
recent developments in individual fairness methods and corresponding fair
metric learning algorithms.
