Explainability and fairness of RegTech for regulatory enforcement: Automated monitoring of consumer complaints

摘要

The application of regulatory technology (RegTech) for monitoring comprehensive data sources has gained increased importance. Nevertheless, previous research neglects that the output of RegTech applications has to be explainable and non-discriminatory. Within this study, we propose design principles and features for a RegTech approach which provides automated assessments of financial consumer complaints. We follow three main design principles. First, we build upon information diagnosticity theory to address the need for explainable classifications empowering regulators to justify their actions. Second, we consider a bag-of-words representation and ensemble learning to ensure high classification accuracy. Third, we take into account author characteristics to avoid discriminating classifications. We evaluate our approach in the financial services industry and show its value for identifying consumer complaints resulting in monetary compensations. The proposed design principles and features are highly relevant for regulators, corporations as well as consumers.