论文标题
信用评分的手机使用数据
Mobile Phone Usage Data for Credit Scoring
论文作者
论文摘要
这项研究的目的是将手机使用数据数据用于做出预测，并找到最佳的信用评分分类方法，即使数据集很小（2,503个客户）。我们使用不同的分类算法将客户分配为使用移动数据付款和非付款方式，然后将预测结果与实际结果进行比较。有几项相关的工作可公开访问，其中移动数据已用于信用评分，但它们都是基于大型数据集的。小公司无法使用与这些相关论文使用的数据集一样大，因此这些研究对它们几乎没有用。在本文中，我们试图争辩说，即使数据集很小，手机使用数据中有价值。我们发现，只有一个仅基于2503个客户的移动数据组成的数据集，我们可以预测信用风险。最好的分类方法给了我们0.62 AUC（曲线下的区域）。
The aim of this study is to demostrate that mobile phone usage data can be used to make predictions and find the best classification method for credit scoring even if the dataset is small (2,503 customers). We use different classification algorithms to split customers into paying and non-paying ones using mobile data, and then compare the predicted results with actual results. There are several related works publicly accessible in which mobile data has been used for credit scoring, but they are all based on a large dataset. Small companies are unable to use datasets as large as those used by these related papers, therefore these studies are of little use for them. In this paper we try to argue that there is value in mobile phone usage data for credit scoring even if the dataset is small. We found that with a dataset that consists of mobile data based only on 2,503 customers, we can predict credit risk. The best classification method gave us the result 0.62 AUC (area under the curve).
