Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part because they lack transparency and explainability which are important factors in establishing reliable technology and the research on this topic with a specific focus on applications in credit risk management. In this paper, we implement two advanced post-hoc model agnostic explainability techniques called Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine learning (ML)-based credit scoring models applied to the open-access data set offered by the US-based P2P Lending Platform, Lending Club. Specifically, we use LIME to explain instances locally and SHAP to get both local and global explanations. We discuss the results in detail and present multiple comparison scenarios by using various kernels available for explaining graphs generated using SHAP values. We also discuss the practical challenges associated with the implementation of these state-of-art eXplainabale AI (XAI) methods and document them for future reference. We have made an effort to document every technical aspect of this research, while at the same time providing a general summary of the conclusions.
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）
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）；Data Juice Lab sp. z o.o.（Data Juice Lab 有限责任公司）；Polish Credit Bureau S. A. (Biuro Informacji Kredytowej S.A.)（波兰信贷局）
Enhancing ML Models Interpretability for Credit Scoring
Dept. of Computer Science, Delft University of Technology（代尔夫特理工大学计算机科学系）；Dept. of Applied Mathematics, Delft University of Technology（代尔夫特理工大学应用数学系）；FF Quant Advisory B.V.（FF量化咨询公司）
Neural Networks for Portfolio-Level Risk Management: Portfolio Compression, Static Hedging, Counterparty Credit Risk Exposures and Impact on Capital Requirement
Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm
The Fu Foundation School of Engineering and Applied Science, Columbia University（哥伦比亚大学傅氏基金工程与应用科学学院）；School of Integrated Circuit Science and Engineering (Exemplary School of Microelectronics), University of Electronic Science and Technology of China（电子科技大学集成电路科学与工程学院（示范性微电子学院））；Washington University in St. Louis, Olin Business School（圣路易斯华盛顿大学奥林商学院）；School of Computer Science & Engineering (School of Cybersecurity), University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院（网络空间安全学院））
DTD-VAE: Disentangled Temporal Dependencies VAE for Credit Risk Prediction
China Minsheng Bank（中国民生银行）；Longying Zhida (Beijing) Technology（龙影智达（北京）科技）；Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学先进技术与网络空间研究院）；College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）
