Credit Risk Scoring: A Stacking Generalization Approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementCredit risk regulation has been receiving tremendous attention, as a result of the effects of the latest global financial crisis. According to the developments made in the Internal Rating Based approach, under the Basel guidelines, banks are allowed to use internal risk measures as key drivers to assess the possibility to grant a loan to an applicant. Credit scoring is a statistical approach used for evaluating potential loan applications in both financial and banking institutions. When applying for a loan, an applicant must fill out an application form detailing its characteristics (e.g., income, marital status, and loan purpose) that will serve as contributions to a credit scoring model which produces a score that is used to determine whether a loan should be granted or not. This enables faster and consistent credit approvals and the reduction of bad debt. Currently, many machine learning and statistical approaches such as logistic regression and tree-based algorithms have been used individually for credit scoring models. Newer contemporary machine learning techniques can outperform classic methods by simply combining models. This dissertation intends to be an empirical study on a publicly available bank loan dataset to study banking loan default, using ensemble-based techniques to increase model robustness and predictive power. The proposed ensemble method is based on stacking generalization an extension of various preceding studies that used different techniques to further enhance the model predictive capabilities. The results show that combining different models provides a great deal of flexibility to credit scoring models

Research on Personal Credit Scoring Model Based on Decision Tree Approach

近年来，随着经济的高速发展，国内信用卡业务越来越繁忙。据一份对2013年中国信用卡市场预测报告（RNCOS，2009）显示，中国银行业在2008年期间发行了超过5000万张的信用卡，累计发行量超过1.5亿张，且这些数字在后续几年有望持续上升。面对如此巨大的业务量，信用卡业务管理层需要一些非常有效的决策工具来辅助他们。而信用评分系统作为一个实用的金融工具，在信用卡业务上有着巨大的应用空间。因此，在中国信用评分系统研究还不够成熟的阶段，研究高效的信用评分系统是一项非常有实际应用价值的工作。 从数据挖掘的技术角度来看，信用评分问题是一个分类问题，目前已有大量数据挖掘分类技术应用到信用评分问题的研究...In recent years, credit card becomes more and more popular with the changing of consumption concept in China. There are more than 50 millions credit cards issued during 2008 in China, taking the total number of credit cards in circulation to over 150 millions. These numbers are projected to continue growing in the next few years. For the decision-makers, they need some help to decide whether to gr...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机软件与理论学号：2302007115131

Customer retention

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018The aim of this study is to model the probability of a customer to attrite/defect from a bank where, for example, the bank is not their preferred/primary bank for salary deposits. The termination of deposit inflow serves as the outcome parameter and the random forest modelling technique was used to predict the outcome, in which new data sources (transactional data) were explored to add predictive power. The conventional logistic regression modelling technique was used to benchmark the random forest’s results. It was found that the random forest model slightly overfit during the training process and loses predictive power during validation and out of training period data. The random forest model, however, remains predictive and performs better than logistic regression at a cut-off probability of 20%.MT 201