In recent years, Support Vector Machines (SVMs) were successfully applied to a wide range of applications. Their good performance is achieved by an implicit non-linear transformation of the original problem to a high-dimensional (possibly infinite) feature space in which a linear decision...

An important objective of data mining is the development of predictive models. Based on a number of observations, a model is constructed that allows the analysts to provide classifications or predictions for new observations. Currently, most research focuses on improving the accuracy or...

Based on two datasets containing Loss Given Default (LGD) observations of home equity and corporate loans, we consider non-linear and non-parametric techniques to model and forecast LGD. These techniques include non-linear Support Vector Regression (SVR), a regression tree and a two-stage model...

Traditionally, customer credit scoring aimed at distinguishing good payers from bad payers at the time of the loan application. However, the timing when customers become bad is also very interesting to investigate since it can provide the bank with the ability to compute the profitability over a...

Traditionally, credit scoring aimed at distinguishing good payers from bad payers at the time of the application. The timing when customers default is also interesting to investigate since it can provide the bank with the ability to do profit scoring. Analysing when customers default is...

he Basel II capital accord encourages financial institutions to develop rating systems for assessing the risk of default of their credit portfolios in order to better calculate the minimum regulatory capital needed to cover unexpected losses. In the internal ratings based approach, financial...