Corporate credit rating is an analysis of credit risks within a corporation, which plays a vital role during the management of financial risk. Traditionally, the rating assessment process based on the historical profile of corporation is usually expensive and complicated, which often takes months. Therefore, most of the corporations, which are lacking in money and time, can't get their own credit level. However, we believe that although these corporations haven't their credit rating levels (unlabeled data), this big data contains useful knowledge to improve credit system. In this work, its major challenge lies in how to effectively learn the knowledge from unlabeled data and help improve the performance of the credit rating system. Specifically, we consider the problem of adversarial semi-supervised learning (ASSL) for corporate credit rating which has been rarely researched before. A novel framework adversarial semi-supervised learning for corporate credit rating (ASSL4CCR) which includes two phases is proposed to address these problems. In the first phase, we train a normal rating system via a normal machine-learning algorithm to give unlabeled data pseudo rating level. Then in the second phase, adversarial semi-supervised learning is applied uniting labeled data and pseudo-labeled data. To demonstrate the effectiveness of the proposed ASSL4CCR, we conduct extensive experiments on the Chinese public-listed corporate rating dataset, which proves that ASSL4CCR outperforms the state-of-the-art methods consistently.
Center for Research on Intelligent Perception and Computing（智能感知与计算研究中心）；National Laboratory of Pattern Recognition（模式识别国家实验室）；Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）
Corporate credit rating reflects the level of corporate credit and plays a crucial role in modern financial risk control. But real-world credit rating data usually shows long-tail distributions, which means heavy class imbalanced problem challenging the corporate credit rating system greatly. To tackle that, inspried by the recent advances of pre-train techniques in self-supervised representation learning, we propose a novel framework named Contrastive Pre-training for Corporate Credit Rating (CP4CCR), which utilizes the self-surpervision for getting over class imbalance. Specifically, we prop…
Brandeis University（布兰迪斯大学）；Guardian Life Insurance（守护者人寿保险公司）
Corporate credit ratings issued by third-party rating agencies are quantified assessments of a company's creditworthiness. Credit Ratings highly correlate to the likelihood of a company defaulting on its debt obligations. These ratings play critical roles in investment decision-making as one of the key risk factors. They are also central to the regulatory framework such as BASEL II in calculating necessary capital for financial institutions. Being able to predict rating changes will greatly benefit both investors and regulators alike. In this paper, we consider the corporate credit rating migr…
There is a massive underserved market for small business lending in the US with the Federal Reserve estimating over \$650B in unmet annual financing needs. Assessing the credit risk of a small business is key to making good decisions whether to lend and at what terms. Large corporations have a well-established credit assessment ecosystem, but small businesses suffer from limited publicly available data and few (if any) credit analysts who cover them closely. We explore the applicability of (DL-based) large corporate credit risk models to small business credit rating.
Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel en…
CreditARF: A Framework for Corporate Credit Rating with Annual Report and Financial Feature Integration
School of Cyberspace Security, Beijing University of Posts and Telecommunications（网络安全学院，北京邮电大学）；Guotai Junan Securities（国泰君安证券）；Hebei University of Technology（河北工业大学）
School of Astronautics, Beihang University（北京航空航天大学宇航学院）
Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations wit…
Stevens Institute of Technology（斯蒂文斯理工学院）；Hanlon Financial Laboratories（汉伦金融实验室）
Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selec…
ShanghaiTech University（上海科技大学）；Tencent（腾讯）；The Hong Kong University of Science and Technology（香港科技大学）
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…
Erasmus University Rotterdam（伊拉斯姆斯大学鹿特丹分校）；ERIM（伊拉斯姆斯管理研究所）；Tinbergen Institute（丁伯根研究所）；CREATES（高级经济时间序列研究中心）
Sovereign credit ratings summarize the creditworthiness of countries. These ratings have a large influence on the economy and the yields at which governments can issue new debt. This paper investigates the use of a Multilayer Perceptron (MLP), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naïve Bayes (NB), and an Ordered Logit (OL) model for the prediction of sovereign credit ratings. We show that MLP is best suited for predicting sovereign credit ratings, with a random cross-validated accuracy of 68%, followed by CART (59%), SVM (41%), NB (38%), and OL (33%). Inve…
Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update older ones. Therefore, credit scoring assessments using artificial intelligence has gained a lot of interest in recent years. Successful machine learning methods can provide rapid analysis of credit scores while updating older ones on a daily time scale. Related studies have shown that neural networks and support vector machines outperform other techniques by pr…
In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the spar…
Central University of Finance and Economics（中央财经大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are gene…
Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline
Division of International Business and Technology Studies, A.R. Sánchez Jr. School of Business, Texas A&M International University（德克萨斯A&M国际大学A.R. Sánchez Jr.商学院国际商务与技术研究部）；Dept. of Management and Marketing, Angelo State University（安吉洛州立大学管理与市场营销系）；Department of Management and Marketing, Southern University and A&M College（南方大学与农工学院管理与市场营销系）；Department of METE, Firat University（菲拉特大学METE系）；Department of Accounting, Western Washington University（西华盛顿大学会计系）
University of California - Irvine（加州大学欧文分校）；Bank of Communications BBM（交通银行BBM）
We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. 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 consi…
With the success of big data and artificial intelligence in many fields, the applications of big data driven models are expected in financial risk management especially credit scoring and rating. Under the premise of data privacy protection, we propose a projected gradient-based method in the vertical federated learning framework for the traditional scorecard, which is based on logistic regression with bounded constraints, namely FL-LRBC. The latter enables multiple agencies to jointly train an optimized scorecard model in a single training session. It leads to the formation of the model with …
The University of Western Ontario（西安大略大学）；University of Basque Country（巴斯克大学）；University of Chile（智利大学）
The thin-file borrowers are customers for whom a creditworthiness assessment is uncertain due to their lack of credit history; many researchers have used borrowers' relationships and interactions networks in the form of graphs as an alternative data source to address this. Incorporating network data is traditionally made by hand-crafted feature engineering, and lately, the graph neural network has emerged as an alternative, but it still does not improve over the traditional method's performance. Here we introduce a framework to improve credit scoring models by blending several Graph Representa…
Università di Bologna（博洛尼亚大学）；Bergische Universität Wuppertal（伍珀塔尔大学）
In this paper, we model the rating process of an entity by using a geometrical approach. We model rating transitions as an SDE on a Lie group. Specifically, we focus on calibrating the model to both historical data (rating transition matrices) and market data (CDS quotes) and compare the most popular choices of changes of measure to switch from the historical probability to the risk-neutral one. For this, we show how the classical Girsanov theorem can be applied in the Lie group setting. Moreover, we overcome some of the imperfections of rating matrices published by rating agencies, which are …
Foundation Models for Credit Risk Prediction: A Game Changer?
Faculty of Economics and Business, KU Leuven, Belgium（比利时库勒万大学经济与商业学院）；School of Business and Economics, Humboldt University of Berlin, Germany（德国洪堡大学商学院）；Department of Statistical and Actuarial Sciences, Western University, Canada（加拿大西部大学统计与精算科学系）；Department of Engineering Management, University of Antwerp, Belgium（比利时安特卫普大学工程管理系）；Business School, University of Edinburgh, United Kingdom（英国爱丁堡大学商学院）；Business School, University of Southampton, United Kingdom（英国南安普顿大学商学院）；School of Mathematical Sciences, University of Southampton, United Kingdom（英国南安普顿大学数学科学学院）；Department of Business Informatics and Operations Management, Ghent University, Belgium（比利时根特大学商业信息与运营管理系）；Department of Mathematics, University of Antwerp, Belgium（比利时安特卫普大学数学系）；Department of Mathematics, KU Leuven, Belgium（比利时库勒万大学数学系）
