Visual analytics for monitoring credit scoring models

Abstract

Financial institutions use credit Scoring models to predict the default of their customers and assist in decision-making about the granting of credit. As a large volume of credit transactions is generated daily alongside a potential increase in this information with the advent of Open Finance, it is challenging to monitor this information quickly so we can act in case these models lose performance. Considering this context, our research aims to provide a Visual Analytics approach to assist in monitoring credit models. For this, initially, we carried out a systematic review of the literature on the subject and conducted semi-structured interviews with 13 domain experts. Considering the needs raised with this study, we created a prototype called Visual Analytics for monitoring Credit Scoring models (VACS). The main contributions of this work are twofold: The requirements gathered through interviews with specialists, which allowed the analysis of how the models are monitored within financial institutions, something that is not disclosed and that can help in the standardization of the monitoring process; and VACS, which was evaluated by four domain experts who considered it a very complete and easy-to-use tool.