A study by economists Laura Blattner at Stanford University and Scott Nelson at the University of Chicago show that differences in mortgage approval between minority and majority groups is not just down to bias, but to the fact that minority and low-income groups have less data in their credit histories.

Even for those who have a good grip on finances, the credit getting game may be closer to a gamble than to a process. This skew is a major factor in inaccessibility to financial and entrepreneurial support. How can AI come to the rescue of these woes? Before we get to that, let’s zero in on the traditional lending practice and the challenges that come with it.

Let’s start with the first step in lending – checking credit scores. Traditional way of credit scoring sources data from bank-based credit data and relevant information from publicly available records – information relating to loans or credit limits, repayment of debt, and inquiries into credit history.

While this seems reliable at a textbook level, this approach may overlook the other layers there are to a person’s credibility.