摘要翻译:
本文旨在提出信用评分技术方法比较的总体思路。任何记分卡都可以根据logistic回归模型中的变量转换以各种方法制作。由于数据的可用性有限,进行比较并提出一种技术优于另一种技术的证据是一个巨大的挑战。在使用来自另一来源的其他数据时,不能保证得出相同的结论。因此,可以提出以下研究挑战:为了得到不受特定数据影响的一般结果,应该如何管理比较?解决办法可能是使用各种随机数据发生器。数据生成器采用两种方法:转移矩阵法和评分法。这里介绍了比较方法的结果和这些比较技术的创建方法。在建立新的模型之前,建模者可以进行一次比较练习,目的是在特定数据的情况下确定最佳方法。本文介绍了Gini、Delta Gini、VIF和最大P值等预测模型的度量方法,强调了一个“好模型”的多准则问题。所建议的想法在模型建立过程中特别有用,在此过程中,有定义的复杂标准试图涵盖模型在一段时间内稳定性的重要问题,以避免危机。提出了选择Logit或WOE方法作为最佳记分卡技术的一些论点。
---
英文标题:
《Consumer finance data generator - a new approach to Credit Scoring
technique comparison》
---
作者:
Karol Przanowski and Jolanta Mamczarz
---
最新提交年份:
2012
---
分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Statistical Finance 统计金融
分类描述:Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类:Quantitative Finance 数量金融学
二级分类:Risk Management 风险管理
分类描述:Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
--
---
英文摘要:
This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due to the limited availability of data. The same conclusion cannot be guaranteed when using other data from another source. The following research challenge can therefore be formulated: how should the comparison be managed in order to get general results that are not biased by particular data? The solution may be in the use of various random data generators. The data generator uses two approaches: transition matrix and scorings. Here are presented both: results of comparison methods and the methodology of these comparison techniques creating. Before building a new model the modeler can undertake a comparison exercise that aims at identifying the best method in the case of the particular data. Here are presented various measures of predictive model like: Gini, Delta Gini, VIF and Max p-value, emphasizing the multi-criteria problem of a "Good model". The idea that is being suggested is of particular use in the model building process where there are defined complex criteria trying to cover the important problems of model stability over a period of time, in order to avoid a crisis. Some arguments for choosing Logit or WOE approach as the best scorecard technique are presented.
---
PDF链接:
https://arxiv.org/pdf/1210.0057