Traditional credit scoring to support lending businesses depends on mathematics called logistic regression to analyze 20 to 40 variables to support a decision. That math is effective when examining the credit histories of consumers in the prime or super-prime categories. It is less effective when considering consumers with little or no credit histories­—thin file, no file, or subprime prospects. Complex machine learning mathematics combined with what is effectively unlimited computer power is now available to make predictions on the creditworthiness of thin file, no file, or subprime pro...