# Inspecting the Process of Bank Credit Rating via Visual Analytics

arXiv: https://arxiv.org/abs/2108.03011 · HTML: https://mlsanity.com/paper/2108.03011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection](https://mlsanity.com/paper/1808.09074/) (2018)
- [RankBooster: Visual Analysis of Ranking Predictions](https://mlsanity.com/paper/2004.06435/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Design Space for Explainable Ranking and Ranking Models](https://mlsanity.com/paper/2205.15305/) (2022)

## Abstract (arXiv)

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.