Thesis, LLM--University of Pretoria, 2005. / Includes bibliographical references. Available on the Internet via the World Wide Web.

Improving access and building inclusive financial systems is not just a goal but also a necessity for economies at every level of development. Unsecured lending was first introduced with the intention of addressing society's ills yet recent violence experienced at the Marikana mines in Rustenburg aroused the attention of both the general public and government. The impact of unsecured lending on the financial wellbeing of consumers has subsequently been brought under the spotlight. Unsecured lending has taken and will continue to take an increasingly central role in our social, political and economic landscape. This study intended to determine the impact of unsecured lending on the financial wellbeing of consumers and made use of three research hypotheses towards this aim. A survey was used to gather data which was statistically analysed by means of a quantitative research strategy. The findings of the research indicated that those who make use of unsecured lending as a means of accessing finance have a better subjective view of their financial well being than those who do not make use of unsecured lending, further, consumers who make use of multiple unsecured loans have an improved outlook with regard to their financial position than those who do not make use of multiple unsecured loans. / Dissertation (MBA)--University of Pretoria, 2013. / pagibs2014 / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted

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Zugl.: Saarbrücken, Univ., Diss., 2007 / Includes bibliographical references (p. 257-272) and index.

In these essays, I study how multidimensional private information causes advantageous selection in a highly concentrated consumer credit market. All three chapters are tightly correlated with each other. I first carefully investigate conditional correlations between choices of a loan type, private default risks, and an additional private information on consumption smoothing motives. I find that their conditional correlations appear consistent with advantageous selection being driven by unobserved heterogeneity in consumption smoothing motives. Then I document how moral hazard links two dimensions of private information: consumption smoothing motives and default risks. By separately identifying moral hazard from adverse selection, I show that consumers with stronger consumption smoothing motives exert more effort to prevent default, generating an endogenous negative association between consumption smoothing motives and default risks. Finally, using a dynamic model of loan type choices and following outcome of default, I recover the joint distribution of bi-dimensional unobserved heterogeneity. This structural estimation also suggests a new way to estimate the inter-temporal elasticity of substitution that represents heterogeneous consumption smoothing motives. As well as being consistent with the results of previous chapters, the results of the structural estimation reveal a strong and positive correlation between inter-temporal elasticity of substitution and default risks.

This thesis is concerned with statistical aspects of credit scoring, the process of determining how likely an applicant for credit is to default with repayments. In Chapters 1-4 a detailed introduction to credit scoring methodology is presented, including evaluation of previous published work on credit scoring and a review of discrimination and classification techniques. In Chapter 5 we describe different approaches to measuring the absolute and relative performance of credit scoring models. Two significance tests are proposed for comparing the bad rate amongst the accepts (or the error rate) from two classifiers. In Chapter 6 we consider different approaches to reject inference, the procedure of allocating class membership probabilities to the rejects. One reason for needing reject inference is to reduce the sample selection bias that results from using a sample consisting only of accepted applicants to build new scorecards. We show that the characteristic vectors for the rejects do not contain information about the parameters of the observed data likelihood, unless extra information or assumptions are included. Methods of reject inference which incorporate additional information are proposed. In Chapter 7 we make comparisons of a range of different parametric and nonparametric classification techniques for credit scoring: linear regression, logistic regression, projection pursuit regression, Poisson regression, decision trees and decision graphs. We conclude that classifier performance is fairly insensitive to the particular technique adopted. In Chapter 8 we describe the application of the k-NN method to credit scoring. We propose using an adjusted version of the Eucidean distance metric, which is designed to incorporate knowledge of class separation contained in the data. We evaluate properties of the k-NN classifier through empirical studies and make comparisons with existing techniques.