Inspecting the Process of Bank Credit Rating via Visual Analytics
- URL: http://arxiv.org/abs/2108.03011v1
- Date: Fri, 6 Aug 2021 09:08:00 GMT
- Title: Inspecting the Process of Bank Credit Rating via Visual Analytics
- Authors: Qiangqiang Liu, Quan Li, Zhihua Zhu, Tangzhi Ye and Xiaojuan Ma
- Abstract summary: Bank credit rating classifies banks into different levels based on publicly disclosed and internal information.
We propose RatingVis to assist experts in exploring and comparing different bank credit rating schemes.
- Score: 34.55692862750793
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Empowering Many, Biasing a Few: Generalist Credit Scoring through Large
Language Models [53.620827459684094]
Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks.
We propose the first open-source comprehensive framework for exploring LLMs for credit scoring.
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
arXiv Detail & Related papers (2023-10-01T03:50:34Z) - Efficient Commercial Bank Customer Credit Risk Assessment Based on
LightGBM and Feature Engineering [5.6081706361236865]
This paper is based on the customer information dataset of a foreign commercial bank in Kaggle.
We use LightGBM algorithm to build a classifier to classify customers, to help the bank judge the possibility of customer credit default.
arXiv Detail & Related papers (2023-08-17T03:32:38Z) - Flexible categorization for auditing using formal concept analysis and
Dempster-Shafer theory [55.878249096379804]
We study different ways to categorize according to different extents of interest in different financial accounts.
The framework developed in this paper provides a formal ground to obtain and study explainable categorizations.
arXiv Detail & Related papers (2022-10-31T13:49:16Z) - Machine Learning Models Evaluation and Feature Importance Analysis on
NPL Dataset [0.0]
We evaluate how different Machine learning models perform on the dataset provided by a private bank in Ethiopia.
XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data.
arXiv Detail & Related papers (2022-08-28T17:09:44Z) - Should Bank Stress Tests Be Fair? [1.370633147306388]
We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies.
We argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks.
arXiv Detail & Related papers (2022-07-27T06:46:51Z) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z) - Managers versus Machines: Do Algorithms Replicate Human Intuition in
Credit Ratings? [0.0]
We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers.
The input to the algorithms consists of a combination of standard financials and soft information available to bank managers.
Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.
arXiv Detail & Related papers (2022-02-09T01:20:44Z) - Supporting Financial Inclusion with Graph Machine Learning and Super-App
Alternative Data [63.942632088208505]
Super-Apps have changed the way we think about the interactions between users and commerce.
This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
arXiv Detail & Related papers (2021-02-19T15:13:06Z) - Determining Secondary Attributes for Credit Evaluation in P2P Lending [0.0]
We utilize machine learning classification and clustering algorithms to accurately predict a borrower's creditworthiness.
We achieved 65% F1 and 73% AUC on the LendingClub data while identifying key secondary attributes.
arXiv Detail & Related papers (2020-06-08T16:12:00Z) - Super-App Behavioral Patterns in Credit Risk Models: Financial,
Statistical and Regulatory Implications [110.54266632357673]
We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models.
Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
arXiv Detail & Related papers (2020-05-09T01:32:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.