论文标题
基于高斯混合模型的新颖的信用评分分类方法
A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models
论文作者
论文摘要
信用评分是银行和其他金融机构使用的一种快速扩展的分析技术。关于信用评分的学术研究提供了一系列用于区分好借款人的分类技术。本文的主要贡献是基于高斯混合模型引入一种新的信用评分方法。我们的算法将消费者分为被标记为正面或负面的组。根据与每个类相关的概率估算标签。我们将模型应用于来自澳大利亚，日本和德国的现实世界数据库。数值结果表明，不仅我们的模型的性能与他人相当，而且即使没有标准的交叉验证技术，它的灵活性也避免了过度合适。本文开发的框架可以提供一种计算高效且强大的工具，以评估相关金融机构中的消费者默认风险。
Credit scoring is a rapidly expanding analytical technique used by banks and other financial institutions. Academic studies on credit scoring provide a range of classification techniques used to differentiate between good and bad borrowers. The main contribution of this paper is to introduce a new method for credit scoring based on Gaussian Mixture Models. Our algorithm classifies consumers into groups which are labeled as positive or negative. Labels are estimated according to the probability associated with each class. We apply our model with real world databases from Australia, Japan, and Germany. Numerical results show that not only our model's performance is comparable to others, but also its flexibility avoids over-fitting even in the absence of standard cross validation techniques. The framework developed by this paper can provide a computationally efficient and powerful tool for assessment of consumer default risk in related financial institutions.
