Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses gain competitive advantage. There is a vast amount of research in credit risk, but our knowledge, none them uses registry as data source model the probability default for individual clients. The goal this paper evaluate different models create accurate assessment using from real data...

Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilize...

Article history: Received 11 September 2014 Received in revised form 17 April 2015 Accepted 25 April 2015 Available online 2 May 2015 Westudy the impact of sovereign risk on the credit risk of the non-financial corporate sector in the Eurozone using credit default swap data. We show that an increase in sovereign credit spreads is associated with a statistically and economically significant incr...

In this paper, we investigate whether the theoretical default probability measures calculated from Merton’s (1974) structural credit risk model can provide a better way to explain and predict credit rating than traditional statistical models. The empirical results suggest that Merton’s theoretical default measure is not a sufficient statistic of equity market information concerning credit quali...