Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1

Are rating agencies’ assignments opaque? Evidence from international banks

We compare the ability of ordered choice models and support vector machines to model and predict international bank ratings. Although support vector machines can identify significant determinants we argue that ordered choice models are more reliable for this. Our findings suggest that ratings reflect a bank’s financial position, the timing of rating assignment and a bank’s country of origin. Accounting for country effects substantially improves predictive performance. We find that support vector machines can produce considerably better predictions of international bank ratings than ordered choice models due to the formers ability to estimate a large number of country dummies unrestrictedly

Macro-economic factors in credit risk calculations: including time-varying covariates in mixture cure models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is due to the fact that default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modelled, distinct from time of default for the susceptible population. In this paper, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank data set.status: publishe