Commercial loans represent a significant portion of the capital invested in the productive assets of the private sector of the U.S. economy. These investments are not without risks and, thus, quantifying the risk of loss is critical to borrowers and lenders alike. The primary goal of our research was to enhance the credit assessment process by building a credit prediction model that quantifies the probability of a commercial borrower default. Our study tests the hypothesis that financial statement data, stock price information, and industry classification can be used to predict the probability of a public company default.