Charel Theisen

MSc Data Science Student

Comparative analysis of two neural computing models to determine the probability of default of credit card clients

Introduction of this Neural Network coursework (Coding: Matlab) :

The Taiwanese economy faced a major credit card debt crisis during 2006. Many banks wanted to increase their market share, and in doing so, they provided credit cards to unqualified customers (Chou, 2006). Consequently, many customers defaulted and consumer finance confidence decreased heavily. Furthermore, one of the primary drivers’ of 2008 financial crisis were granted loans to people whose risk profile was too high (Charpignon, Horel and Tixier, 2012). To prevent a high number of defaults, which could cause a financial crisis again, it is important to develop models which assist credit managers in determining the risk of customers.

The purpose of this research paper is to critically evaluate two neural computing models which classify customers based on their personal information (such as age, marital status or education) into two binary states, namely default and non-default. Predicting these two states, helps banks to increase their market share and prevent the loss which happens when customers default. The chosen models are Support Vector Machines (SVM) and Neural Network (NN). Many papers already discussed the relationship between customer’s personal information and their default behaviour, suggesting that there is a strong relationship (Lu, Wang and Yoon, 2017).

This paper is organised as follows. Section 2 provides a critical review of SVM and NN techniques. Section 3 delivers information about the used dataset. Section 4 describes the methodology for this research. The results and critically evaluation are presented in Section 5. Finally, this paper ends with a conclusion in Section 6.

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Paper is available upon request.

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