利用机器学习预测银行贷款中的信用风险

Mohit Lakhani, Bhavesh Dhotre, Saurabh Giri
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引用次数: 4

摘要

1、2、3的学生,部门的工程,NMIMS学院,印度马哈拉施特拉邦 ---------------------------------------------------------------------***-------------------------------------------------------------------- 文摘查看当前场景有巨大风险的银行提供贷款。从而减少他们的资本损失;银行在批准贷款前应评估和分析个人的信誉。如果没有这一过程,这笔贷款很有可能在不久的将来变成不良贷款。由于与数据挖掘、数据可用性和计算能力相关的先进技术,大多数银行正在更新其业务模式,并转向机器学习方法。信用风险预测是决策和透明度的关键。在这篇综述文章中,我们讨论了基于机器和深度学习模型的分类器在预测贷款违约概率方面的实际数据。从各种模型中选择最重要的特征,然后在建模过程中通过比较它们在单独数据上的性能来测试二元分类器的稳定性。
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Prediction of credit risks in lending bank loans using machine learning
1,2,3Student, Dept. of IT Engineering, NMIMS College, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Looking at the current scenario there are huge risks involved for Banks to provide Loans. So as to reduce their capital loss; banks should assess and analyse credibility of the individual before sanctioning loan. In the absence of this process there are many chances that this loan may turn in to bad loan in near future. Due to the advanced technology associated with Data mining, data availability and computing power, most banks are renewing their business models and switching to Machine Learning methodology. Credit risk prediction is key to decision-making and transparency. In this review paper, we have discussed classifiers based on Machine and deep learning models on real data in predicting loan default probability. The most important features from various models are selected and then used in the modelling process to test the stability of binary classifiers by comparing their performance on separate data.
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