small loans. Simply relying heavily on statistical tools of traditional credit reporting agencies to serve the so-called ‘under-banked’ will not work going forward. By contrast, machine learning can actually categorize the growing list of non-traditional borrowers, such as the rising tide of millenials, that are often not catered to and who are highly accustomed to helping themselves with various self service digital tools. Machine learning not only automates and therefore expedites the credit risk determination process, but also improves the accuracy of the process by factoring in a far wider range of relevant data than static statistical models could ever process. But perhaps most importantly, machine learning can leverage new data as it arrives to change the parameters of credit worthiness. For example, gentri- fication of downtrodden neighborhoods is happening very rapidly in some major US cities. Thus the weighting of zip codes and street addresses should change accordingly. In a static statistical model of credit worthiness, they don’t. With machine learning, the algorithm recognizes the broader demographic changes underway, factoring that into the overall credit calculus. Machine learning not only automates and therefore expedites the credit risk determination process, but also improves the accuracy of the process by factoring in a far wider range of relevant data than static statistical models could ever process.
More automation is needed to lessen the dependence on expensive manual processes.
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