基于稀疏逻辑回归的信用风险评估模型
Credit Risk Assessment Model Based on Sparse Logistic Regression

随着经济的持续增长和金融科技的不断发展，个人信贷作为一种满足消费需求的金融工具，其市场规模自然随之扩大。受到经济下行压力、不良贷款行为增加与各种突发变故的影响，个人信贷违约率逐渐上升，一个完善且高效的个人信用评估模型其重要性不言而喻。在信用评估过程中，通过一系列的具体指标和因素去判断个人的信用风险，在庞大的市场规模下，需要巨量的资源投入。本文提出了一种基于稀疏优化的逻辑回归模型，其能在保持一定准确度的情况下快速地得出个人风险评估结果。最后通过真实数据，验证所提出稀疏逻辑回归模型的有效性。
With the continuous growth of the economy and the development of financial technology, the market scale of personal credit, as a financial tool to satisfy consumer demand, has naturally expanded. Influenced by the economic downward pressure, the increase of non-performing loan behaviors and various unexpected changes, the default rate of personal credit is gradually rising, and the importance of a perfect and efficient personal credit assessment model is self-evident. In the process of credit assessment, a series of specific indicators and factors are used to judge the credit risk of an individual, which requires a huge amount of resources under a huge market scale. In this paper, a logistic regression model based on sparse optimization is proposed, which can quickly produce individual risk assessment results while maintaining a certain degree of accuracy. Finally, the effectiveness of the proposed sparse logistic regression model is verified by real data.