英文标题：
《Deep Generative Models for Reject Inference in Credit Scoring》
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作者：
Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert
Jenssen
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最新提交年份：
2021
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英文摘要：
Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring.
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中文摘要：
基于公认应用程序的信用评分模型可能存在偏差，其后果可能会产生统计和经济影响。拒绝推断是试图推断被拒绝申请的信誉状态的过程。在本研究中，我们使用深层生成模型开发了两个新的半监督贝叶斯模型，用于信用评分中的拒绝推理，其中我们将数据生成过程建模为依赖于高斯混合。目标是通过添加拒绝应用程序来提高信用评分模型中的分类精度。我们提出的模型通过精确列举贷款的两种可能结果（违约或非违约），推断出被拒绝申请的未知信用度。深层生成模型中使用的高效随机梯度优化技术使我们的模型适用于大型数据集。最后，本研究中的实验表明，我们提出的模型在信用评分拒绝推理方面的性能优于经典和替代机器学习模型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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