The subprime borrower market consists of individuals and households who cannot qualify for prime financing terms, because of their low credit scores. The range of their credit scores is rather small, rendering the subprime borrower market a uniquely homogenous segment. The homogeneity of the subprime segment causes the loan-decision factors – e.g., risk of default characteristics, the absence of collateral, charge-off rates, purposes of loan, property types, and current market conditions – to be tightly “knotted” (highly correlated), a condition that is not favorable for building any scoring model. Statistical methods are virtually unproductive at untying the knotty relationships among loan-decision factors. The purpose of this article is to present an alternative machine learning method – the GenIQ© Model – that has the ability to unlace (data mine) the knotty relationship among loan-decision factors, producing an impressively predictive subprime lender scoring model.