  Financial decisions impact our lives, and thus everyone from the regulator to
the consumer is interested in fair, sound, and explainable decisions. There is
increasing competitive desire and regulatory incentive to deploy AI mindfully
within financial services. An important mechanism towards that end is to
explain AI decisions to various stakeholders. State-of-the-art explainable AI
systems mostly serve AI engineers and offer little to no value to business
decision makers, customers, and other stakeholders. Towards addressing this
gap, in this work we consider the scenario of explaining loan denials. We build
the first-of-its-kind dataset that is representative of loan-applicant friendly
explanations. We design a novel Generative Adversarial Network (GAN) that can
accommodate smaller datasets, to generate user-friendly textual explanations.
We demonstrate how our system can also generate explanations serving different
purposes: those that help educate the loan applicants, or help them take
appropriate action towards a future approval.
