Inspecting the Process of Bank Credit Rating via Visual Analytics

TL;DR

This paper introduces RatingVis, a visual analytics tool designed to help financial experts explore, compare, and understand various bank credit rating schemes by incorporating domain knowledge and bank types.

Contribution

The paper presents a novel interactive visual analytics system that infers indicator weights and considers bank types, addressing gaps in existing credit rating analysis methods.

Findings

Experts found RatingVis improved understanding of rating schemes.

Case study demonstrated effective comparison of bank credit ratings.

Involving domain knowledge enhanced indicator weight inference.

Abstract

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.