Exploring responsible applications of deep learning for consumer credit risk assessment

目的探索如何以负责任的方式将深度神经网络应用于消费者信用风险评估。研究问题：将深度学习作为信用风险评估的传统工具是否能使我们受益？我们如何设计可解释的深度神经网络算法，以便在贷款部门做出负责任的决策？概述和背景：随着信用卡和其他贷款需求的增加，银行等贷款人面临着有效衡量借款人贷款风险的挑战。金融业正在不断研究，努力构建机器学习信用评分模型来自动评估消费者信用风险，这是银行解决这一问题的典型方法。近年来，深度神经网络模型在目标识别、自然语言处理和姿态估计等领域得到了广泛的应用。然而，贷款行业对深度神经网络的关注一直很少。原因是，它们在信用评分等决策系统中的使用引发了信任、问责和公平的问题。这项研究的重点是探索如何使用深度神经网络模型以透明的方式做出贷款决策，以促进信任并确保算法在信贷行业负责任地使用。

AimTo explore how to apply deep neural networks for consumer credit risk assessment in a responsible manner.Research Questions:Can we benefit from using deep learning as a traditional tool for credit risk assessment?How can we design an interpretable deep neural network algorithm for responsible decision making in the lending sector?Overview and Context:With an increased demand for credit cards and other loans, lenders such as banks are faced with the challenge of effectively measuring the risk of lending to borrowers. The financial industry is continuously researching efforts to make Constructing machine learning credit scoring models to assess consumer credit risk automatically is a typical approach banks use to tackle the issue. In recent years, there has been a widespread adoption of deep neural network models in many application areas as object recognition, natural language processing and pose estimation. However, there has been minimal attention given to deep neural networks in the lending sector. The reason being their use in decision making systems such as credit scoring raises questions of trust, accountabilty and fairness. The focus of this research is to explore how deep neural network models can be used to make lending decisions in a transparent way that promotes trust and ensures responsible use of the algorithm in the credit industry.