重组信贷流程:德国企业行为评分

Sebastian Fritz, Detlef Hosemann
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引用次数: 30

摘要

提出了一种基于企业经常账户数据的月度信用状况自动分析工具。使用不同的统计和机器学习方法来开发监督债务人的评分模型。选择以下方法进行模型开发:线性判别分析模式识别(k-nearest- neighbors)遗传算法神经网络决策树所开发的模型不仅在分类结果上进行比较,而且在分数分布、透明度和it实现方面进行比较。版权所有©2000约翰威利父子有限公司
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Restructuring the credit process: behaviour scoring for german corporates
An instrument for automated monthly credit standing analysis based on data of the corporates current accounts is presented. Different methods of statistics and machine learning are used to develop scoring models for the supervision of debtors. The following methods were selected for model developement: Linear Discriminant Analysis Pattern Recognition (k-nearest-neighbours) Genetic Algorithms Neural Networks Decision Trees The developed models were compared not only concerning their classification results but also concerning score distribution, transparency and IT-realisation. Copyright © 2000 John Wiley & Sons, Ltd.
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