Data Mining Techniques in Predicting Default Rates

Authors: Jozef Zurada

Publish Date: 2002

Volume: , Issue: , Pages: 285-296

PDF Link

Abstract

The paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon The paper uses three different data mining techniques decision trees neural networks and logistic regression and the ensemble model which combines the three techniques to predict whether a particular customer defaulted or paid off his/her loan The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans

Keywords: