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Determination of Default Probability in Auto Finance through Predictive Analytics.

机译：通过预测分析确定汽车金融中的默认概率。

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摘要

Banks and credit card lenders employ a system known as credit scoring to quantify the risk factors associated with each potential borrower. An excellent credit score usually assures the borrower's ability and willingness to pay her/his loan. Due to the massive number of applications received daily as well as an increasing number of governmental regulatory requirements, credit scoring has become a standard in the banking industry. In this thesis, the concept of credit scoring and the theory and statistics behind it are explained thoroughly. In the application sections, different statistical methods, such as logistic regression, discriminant function analysis, binary decision tree analysis, and artificial neural networks are used to analyze real data collected from a credit bureau. The results and models developed from these different analyses are then compared to determine the best method for developing a credit score model. Due to the inherently large number of attributes associated with each loan borrower provided by the credit bureau, a principal component analysis is first used to reduce significantly the number of variables that will be considered for inclusion in the credit score model. Three selection methods such as forward selection, backward elimination, and stepwise regression are also utilized to determine which subset of variables is to be included in the final model. The conclusion of the thesis discusses the best method among the four mentioned statistical methods used to analyze the data, and reveals the best final credit score model for this study.
机译：银行和信用卡贷方使用一种称为信用评分的系统来量化与每个潜在借款人相关的风险因素。良好的信用评分通常可以确保借款人的能力和偿还贷款意愿。由于每天收到大量申请，并且政府监管要求越来越高，因此信用评分已成为银行业的标准。本文对信用评分的概念及其背后的理论和统计方法进行了详尽的解释。在应用程序部分中，使用不同的统计方法（例如逻辑回归，判别函数分析，二元决策树分析和人工神经网络）来分析从征信局收集的真实数据。然后比较从这些不同分析得出的结果和模型，以确定开发信用评分模型的最佳方法。由于信用局提供的与每个借款人相关的属性固有的数量众多，因此首先使用主成分分析来显着减少将被考虑纳入信用评分模型的变量数量。还使用三种选择方法（例如正向选择，向后消除和逐步回归）来确定最终模型中将包括哪些变量子集。本文的结论讨论了上述四种用于分析数据的统计方法中的最佳方法，并揭示了该研究的最佳最终信用评分模型。

著录项

  • 作者

    Pham, Huy D.;

  • 作者单位

    California State University, Long Beach.;

  • 授予单位 California State University, Long Beach.;
  • 学科 Statistics.;Finance.
  • 学位 M.S.
  • 年度 2017
  • 页码 69 p.
  • 总页数 69
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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