Abstract: Consumer credit risk assessment is an important aspect of financial risk management and credit industry competition.Credit database often contains noisy data,which makes the data uncertain.Least squares support vector machines,a widely used binary classification model,is simple and easy to be applied.In this paper,we propose a robust weighted adaptive L_p least squares support vector machines,which can deal with unbalanced data sets and noisy data.The empirical test on simulation and three credit data sets have shown the model has out standing robustness and generalization ability.

Key words: credit risk assessment, robust, adaptive, least squares support vector machines