Abstract
Financial institutions use various data mining algorithms to determine the credit limits for individuals using featureslike age, education, employment, gender, income, and marital status. But, there is still a question of accuratepredictability, that is, how accurate can an institution be in predicting risk and granting credit levels. If an institutiongrants too low of a credit limit/loan for an individual, then the institution may lose business to competitors, butif the institution grants too high of a credit limit/loan, then the institution may lose money if that individual doesnot repay the credit/loan. The novelty of this work is that it shows how to improve the accuracy in predictingcredit limits/loan amounts using synthetic feature generation. By creating secondary groupings and includingboth the original binning and the synthetic bins, the classification accuracy and other statistical measures likeprecision 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 andother statistical measures.
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