How banks and financial institutions evaluate prospective borrowers is changing, thanks to big data, machine learning, analytics and artificial intelligence. Lynnette Purda, professor & RBC Fellow of Finance at Smith, says all this technology can actually improve financial inclusion. That’s especially good for young adults and newcomers to Canada, who have traditionally found it difficult to get credit.

But are there drawbacks to alternative credit assessment information?

In this video, Purda explains how technology is shifting the way creditworthiness is gauged. “There’s a lot of data that’s just residing in an individual’s smartphone,” she says, and lenders can use this data and machine learning to determine patterns in an individual’s willingness and ability to repay debt. Lenders can even look to a person’s behaviour and their social network to assess their credit quality.

Purda also sheds light on some of the concerns with big-data credit checking–from data privacy and security issues to potential bias. By harnessing vast amounts of data, machine learning can identify key patterns, ultimately creating a credit quality assessment model that has the potential to be more inclusive. But understanding the challenges is crucial.