Synthetic feature generation to improve accuracy in prediction of credit limits

Abstract

Financial institutions use various data mining algorithms to determine the credit limits for individuals using features like age, education, employment, gender, income, and marital status. But, there is still a question of accurate predictability, that is, how accurate can an institution be in predicting risk and granting credit levels. If an institution grants too low of a credit limit/loan for an individual, then the institution may lose business to competitors, but if the institution grants too high of a credit limit/loan, then the institution may lose money if that individual does not repay the credit/loan. The novelty of this work is that it shows how to improve the accuracy in predicting credit limits/loan amounts using synthetic feature generation. By creating secondary groupings and including both the original binning and the synthetic bins, the classification accuracy and other statistical measures like precision and ROC improved substantially. Hence, our research showed that without synthetic feature generation, the classification rates were low, and the use of synthetic features greatly improved the classification accuracy and other statistical measures.