Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment
New York University（纽约大学）；Brown University（布朗大学）；Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California, Irvine（加州大学欧文分校）
This paper delves into the application of adversarial domain adaptation (ADA) for enhancing credit risk assessment in financial institutions. It addresses two critical challenges: the cold start problem, where historical lending data is scarce, and the data imbalance issue, where high-risk transactions are underrepresented. The paper introduces an improved ADA framework, the Wasserstein Distance Weighted Adversarial Domain Adaptation Network (WD-WADA), which leverages the Wasserstein distance to align source and target domains effectively. The proposed method includes an innovative weighted strategy to tackle data imbalance, adjusting for both the class distribution and the difficulty level of predictions. The paper demonstrates that WD-WADA not only mitigates the cold start problem but also provides a more accurate measure of domain differences, leading to improved cross-domain credit risk assessment. Extensive experiments on real-world credit datasets validate the model's effectiveness, showcasing superior performance in cross-domain learning, classification accuracy, and model stability compared to traditional methods.
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.
Hidden States Know Where Reasoning Diverges: Credit Assignment via Span-Level Wasserstein Distance
Beijing University of Posts and Telecommunications（北京邮电大学）；Meituan（美团）；Tianjin University（天津大学）；Renmin University of China（中国人民大学）；The University of Melbourne（墨尔本大学）；University of International Business and Economics（国际经贸大学）
Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning
Shreya Department of Computer Science and Engineering Specialisation in Data Science and Artificial Intelligence（Shreya 计算机科学与工程系 数据科学与人工智能专攻）；Mr. Harsh Pathak Assistant Professor Department of Computer Science and Engineering（Harsh Pathak 助理教授 计算机科学与工程系）；SRM University, Sonepat, Haryana, India（SRM 大学, Sonepat, Haryana, India）
Auto-encoders are among the most popular neural network architecture for dimension reduction. They are composed of two parts: the encoder which maps the model distribution to a latent manifold and the decoder which maps the latent manifold to a reconstructed distribution. However, auto-encoders are known to provoke chaotically scattered data distribution in the latent manifold resulting in an incomplete reconstructed distribution. Current distance measures fail to detect this problem because they are not able to acknowledge the shape of the data manifolds, i.e. their topological features, and …
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）
For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower's historical data is scarce or not available. This study aims to understand the creditworthiness assessment performa…
The University of Western Ontario（西安大略大学）；University of Basque Country（巴斯克大学）；Business Intelligence Research Center (CEINE)（商业智能研究中心（CEINE））；University of Chile（智利大学）
Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increas…
The rise of algorithmic decision-making has spawned much research on fair machine learning (ML). Financial institutions use ML for building risk scorecards that support a range of credit-related decisions. Yet, the literature on fair ML in credit scoring is scarce. The paper makes three contributions. First, we revisit statistical fairness criteria and examine their adequacy for credit scoring. Second, we catalog algorithmic options for incorporating fairness goals in the ML model development pipeline. Last, we empirically compare different fairness processors in a profit-oriented credit scori…
Credit assessments activities are essential for financial institutions and allow the global economy to grow. Building robust, solid and accurate models that estimate the probability of a default of a company is mandatory for credit insurance companies, moreover when it comes to bridging the trade finance gap. Automating the risk assessment process will allow credit risk experts to reduce their workload and focus on the critical and complex cases, as well as to improve the loan approval process by reducing the time to process the application. The recent developments in Artificial Intelligence a…
Université Libre de Bruxelles（布鲁塞尔自由大学）；Worldline（世界线）
Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an …
Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research
Huacong Qingjiao Information Technology (Beijing) Co., Ltd.（华聪青教信息技术（北京）有限公司）；University of Maine at Presque Isle（缅因大学普雷斯克艾尔分校）；San Francisco Bay University（旧金山湾大学）；Universitario Tecnológico Universitam；Northern Arizona University（北亚利桑那大学）
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minority class. Standard methods like SMOTE rely on finding nearest neighbours and linear interpolations which are problematic in case of high-dimensional, complex data distributions. Generative Adversarial Networks (GANs) have been proposed as an alternative method for generating artificial minority examples as they can model complex distributions. However, prior research on GAN-based oversampling does not incorporate rece…
Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predictions made by a neural network. We provide an initial investigation into these techniques for the assessment of credit risk with neural networks.
Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
School of Management, Fudan University（复旦大学管理学院）；Department of Decision Analytics and Operations, College of Business, City University of Hong Kong（城市大学商学院决策分析与运作部门）；Department of Industrial and Systems Engineering, University of Minnesota（明尼苏达大学工业与系统工程系）；The Laboratory for AI-Powered Financial Technologies（人工智能金融技术实验室）
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；The University of Edinburgh（爱丁堡大学）
In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each other. The objective of the generator is to confuse the discriminator. The objective of the discriminator is to distinguish the instances coming from the generator and the instances coming from the original dataset. By training GANs on a set of credit card fraudulent transactions,…
Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；University of Chinese Academic of Science（中国科学院大学）
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 maj…
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
University of California, Irvine（加州大学欧文分校）；Rice University（莱斯大学）；New York University（纽约大学）；University of Chicago（芝加哥大学）；Trine University（特莱恩大学）；Columbia University（哥伦比亚大学）
Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Beijing Key Laboratory of Fault-Tolerant Quantum Computing（北京容错量子计算重点实验室）；Beijing Academy of Quantum Information Sciences（北京量子信息科学研究院）；Longying Zhida (Beijing) Technology Co., Ltd.（长岭智达（北京）科技有限公司）；Beijing National Laboratory for Condensed Matter Physics（北京凝聚态物理国家实验室）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Hefei National Laboratory（合肥国家实验室）；Yale School of Management, Yale University（耶鲁大学管理学院）；Songshan Lake Materials Laboratory（松山湖材料实验室）
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…
University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. T…
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 Southampton（南安普顿大学）；The University of Western Ontario（西安大略大学）
Compared to consumer lending, Micro, Small and Medium Enterprise (mSME) credit risk modelling is particularly challenging, as, often, the same sources of information are not available. Therefore, it is standard policy for a loan officer to provide a textual loan assessment to mitigate limited data availability. In turn, this statement is analysed by a credit expert alongside any available standard credit data. In our paper, we exploit recent advances from the field of Deep Learning and Natural Language Processing (NLP), including the BERT (Bidirectional Encoder Representations from Transformer…
Duke University（杜克大学）；University of Rochester（罗切斯特大学）；The University of Iowa（爱荷华大学）
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global mo…
Machine learning plays an essential role in preventing financial losses in the banking industry. Perhaps the most pertinent prediction task that can result in billions of dollars in losses each year is the assessment of credit risk (i.e., the risk of default on debt). Today, much of the gains from machine learning to predict credit risk are driven by gradient boosted decision tree models. However, these gains begin to plateau without the addition of expensive new data sources or highly engineered features. In this paper, we present our attempts to create a novel approach to assessing credit ri…
Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学电子工程与计算机科学学院）；Institute for Data Science and Artificial Intelligence, Boğaziçi University（博雅大学数据科学与人工智能研究所）；College of Science Computer Science Department, Kuwait University（科威特大学科学学院计算机科学系）
Reykjavík University（雷克雅未克大学）；The University of Western Ontario（西安大略大学）
We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economic activity) and allows for explicitly modelling the interaction between connected borrowers. We develop a multilayer personalized PageRank algorithm that allows quantifying the strength of the default exposure of any borrower in the network. We test our methodology in an agricultural lending framework, where it has been suspected for a long time default correlates between borrowers when they are subject to the same s…
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…
Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment
China University of Geosciences（中国地质大学）；Walmart Inc.（沃尔玛公司）；Fordham University（福特汉姆大学）
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…
Feature Ranking in Credit-Risk with Qudit-Based Networks
Department of Informatics and Telecommunications（信息与电信系）；National and Kapodistrian University of Athens（国家与卡布里亚大学）；Eulambia Advanced Technologies Ltd（Eulambia先进技术有限公司）
A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks
Department of Statistical and Actuarial Sciences, Western University（西敏大学统计与精算科学系）；University of Southampton Business School（南安普顿大学商学院）；Centre for Operational Research, Management Sciences and Information Systems（运营研究、管理科学与信息系统中心）；School of Mathematical Sciences, University of Southampton（南安普顿大学数学科学学院）；Department of Computer Science, Reykjavík University（雷克雅未克大学计算机科学系）
Borealis AI（北极光人工智能研究院）；Royal Bank of Canada (RBC)（加拿大皇家银行）
This paper is a note on new directions and methodologies for validation and explanation of Machine Learning (ML) models employed for retail credit scoring in finance. Our proposed framework draws motivation from the field of Artificial Intelligence (AI) security and adversarial ML where the need for certifying the performance of the ML algorithms in the face of their overwhelming complexity poses a need for rethinking the traditional notions of model architecture selection, sensitivity analysis and stress testing. Our point of view is that the phenomenon of adversarial perturbations when detac…
Ludwig Maximillian University of Munich（慕尼黑路德维希·马克西米利安大学）；U.S. Food and Drug Administration（美国食品药品监督管理局）；Technical University of Munich（慕尼黑工业大学）
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift. We use Auto Encoding Variational Bayes to find a latent representation of the data, on which a Variational Gaussian Mixture Model is applied to deliberately create distribution shift by dividing the dataset into different clusters. Wasserstein distance is used to characterize the extent of distribution shift between the generated data splits. We compare several popular Convolutional Neural Network (CNN) archite…
Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
Department of Electrical and Computer Engineering, Rice University（电气与计算机工程系， Rice大学）；Department of Civil and Environmental Engineering, Rice University（土木与环境工程系， Rice大学）；Ken Kennedy Institute, Rice University（肯尼迪研究所， Rice大学）；Department of Computer Science, Rice University（计算机科学系， Rice大学）
Generative neural network models, including Generative Adversarial Network (GAN) and Auto-Encoders (AE), are among the most popular neural network models to generate adversarial data. The GAN model is composed of a generator that produces synthetic data and of a discriminator that discriminates between the generator's output and the true data. AE consist of an encoder which maps the model distribution to a latent manifold and of a decoder which maps the latent manifold to a reconstructed distribution. However, generative models are known to provoke chaotically scattered reconstructed distribut…
Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid Chengde Power Supply Company（国网承德供电公司）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）
