论文标题
使用香农熵来减少NPA总体NPA并对违约者进行分类
To Reduce Gross NPA and Classify Defaulters Using Shannon Entropy
论文作者
论文摘要
在过去的几年中，非绩效资产（NPA）一直受到银行的严重关注。 NPA对银行造成了巨大的损失，因此它成为决定哪些贷款具有成为NPA的能力的极其至关重要的一步，从而决定要授予哪些贷款以及拒绝哪些贷款。在本文中，我们提出了一种算法，该算法旨在非常精心处理财务数据，以非常准确地预测是否将来是否将特定贷款归类为NPA。而不是用于决定哪些贷款可以变成NPA的常规较少准确的分类器，而是使用熵作为基础构建自己的分类器模型。我们使用香农熵创建了一个基于熵的分类器。分类器模型将接受或拒绝的两个类别中的数据点分类。我们利用局部熵和全局熵来帮助我们确定输出。然后将熵分类器模型与用于预测NPA的现有分类器进行比较，从而使我们对性能有所了解。
Non Performing Asset(NPA) has been in a serious attention by banks over the past few years. NPA cause a huge loss to the banks hence it becomes an extremely critical step in deciding which loans have the capabilities to become an NPA and thereby deciding which loans to grant and which ones to reject. In this paper which focuses on the exact crux of the matter we have proposed an algorithm which is designed to handle the financial data very meticulously to predict with a very high accuracy whether a particular loan would be classified as a NPA in future or not. Instead of the conventional less accurate classifiers used to decide which loans can turn to be NPA we build our own classifier model using Entropy as the base. We have created an entropy based classifier using Shannon Entropy. The classifier model categorizes our data points in two categories accepted or rejected. We make use of local entropy and global entropy to help us determine the output. The entropy classifier model is then compared with existing classifiers used to predict NPAs thereby giving us an idea about the performance.
