  Bank credit rating classifies banks into different levels based on publicly
disclosed and internal information, serving as an important input in financial
risk management. However, domain experts have a vague idea of exploring and
comparing different bank credit rating schemes. A loose connection between
subjective and quantitative analysis and difficulties in determining
appropriate indicator weights obscure understanding of bank credit ratings.
Furthermore, existing models fail to consider bank types by just applying a
unified indicator weight set to all banks. We propose RatingVis to assist
experts in exploring and comparing different bank credit rating schemes. It
supports interactively inferring indicator weights for banks by involving
domain knowledge and considers bank types in the analysis loop. We conduct a
case study with real-world bank data to verify the efficacy of RatingVis.
Expert feedback suggests that our approach helps them better understand
different rating schemes.
