Generating User-friendly Explanations for Loan Denials using GANs
This addresses the gap in explainable AI for financial services by providing explanations useful to non-expert stakeholders like customers and business decision makers, though it is incremental in applying GANs to this specific domain.
The paper tackles the problem of generating user-friendly explanations for loan denials by creating a novel dataset and a GAN-based method, demonstrating its ability to produce explanations for education or actionable advice.
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.