Nowadays consumer loan plays an important role in promoting the economic growth, and credit cards are the most popular consumer loan. One of the most essential parts in credit cards is the credit limit management. Traditionally, credit limits are adjusted based on limited heuristic strategies, which are developed by experienced professionals. In this paper, we present a data-driven approach to manage the credit limit intelligently. Firstly, a conditional independence testing is conducted to acquire the data for building models. Based on these testing data, a response model is then built to measure the heterogeneous treatment effect of increasing credit limits (i.e. treatments) for different customers, who are depicted by several control variables (i.e. features). In order to incorporate the diminishing marginal effect, a carefully selected log transformation is introduced to the treatment variable. Moreover, the model's capability can be further enhanced by applying a non-linear transformation on features via GBDT encoding. Finally, a well-designed metric is proposed to properly measure the performances of compared methods. The experimental results demonstrate the effectiveness of the proposed approach.
A Hierarchical User Intention-Habit Extract Network for Credit Loan Overdue Risk Detection
Beijing Trusfort Technology Co.,Ltd.（北京信任度科技有限公司）；Beijing University of Posts and Telecommunications（北京邮电大学）
手机银行APP中出现了更多的个人消费贷款产品，为了方便使用，申请流程往往比较简单，这意味着申请贷款时很少要求用户填写申请信息，不利于构建用户的信用档案。因此，简单的申请流程给逾期风险检测带来了巨大的挑战，较高的逾期率将给银行带来较大的经济损失。本文提出了一种利用手机银行APP用户行为信息的层次化用户意图-习惯提取网络模型HUIHEN（Hierarchical User Intention-Habit Extract Network），针对用户行为的多样性，将行为序列按时间间隔划分为会话，采用场感知的方法提取行为的场内信息。然后，我们提出了一个由时间感知的GRU和用户项目感知的GRU组成的分层网络，以捕获用户的短期意图和用户的长期习惯，可以被视为用户简档的补充。该模型可以在不增加原有在线申请流程复杂度的前提下提高准确率。实验结果证明了HUIHEN的优越性，并表明HUIHEN在所有数据集上都优于其他最先进的模型。
Unifund CCR, LLC（Unifund CCR有限责任公司）；Pamplin College of Business, Virginia Polytechnic Institute（弗吉尼亚理工学院庞普林商学院）；College of Business, University of Cincinnati（辛辛那提大学商学院）
Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs
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）
Geometric Dynamics of Consumer Credit Cycles: A Multivector-based Linear-Attention Framework for Explanatory Economic Analysis
H2O.ai；Center for Trustworthy AI Through Model Risk Management, University of North Carolina Charlotte（可信人工智能中心，北卡罗来纳大学夏洛特分校）；Clare Hall, University of Cambridge（克莱尔学院，剑桥大学）
PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations
OM & QT Area, Indian Institute of Management Indore（印度管理学院印度尔分校OM与QT部门）；Department of Statistics & Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）
Privacy-Preserving Credit Risk Prediction with Alternative Data
School of Management and Economics, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳）管理学院）；School of Management, Fudan University（复旦大学管理学院）；Lerner College of Business and Economics, University of Delaware（特拉华大学勒纳商学院）
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）
