> Affirm makes an assessment of creditworthiness based on a person's name, email, mobile number, birthday and the last four digits of his or her social security number, as well as behavioral factors like how long he or she takes to remember all that information. If that combination doesn’t quite add up to a loan, Affirm may also ask borrowers to share information from other online sources, like a GitHub coding profile or a savings account that shows cash flow history.
It's going to be interesting to see how these lenders show compliance with various aspects of the Fair Credit Reporting Act and Equal Credit Opportunity Act.
The latter, for instance, prohibits lenders from denying credit on the basis of race, color, religion, national origin, gender, marital status, etc. But it's possible some of the data points used by these lenders will turn out to be proxies for these attributes. There is already a lot of discussion around how lenders are using "big data" and the associated potential pitfalls[1][2].
There are two scenarios: Someone whose FICO score indicates more risk than Affirm's algorithm, and someone whose FICO score indicates less risk than Affirm's algorithm.
Affirm chooses a rate based on its algorithm's prediction, and I believe worst case just offers standard FICO-based rates... because it has a slightly different business model, it has no incentive to ding customers with a $15 fee if they pay the third payment three days late.
You're oversimplifying the issue here. The point is that Affirm is apparently using an amalgamation of potentially thousands of data points to make decisions about creditworthiness. Some of these data points, including name, email and mobile number, as well as behavioral factors, could very well prove to be proxies for characteristics which the law prohibits lenders from using to make credit decisions.
Interesting. At some level things like income could be considered a proxy as well... arguably the absence some of the more stringent consumer lenders, like Chase, on one's credit report might predict membership in a disadvantaged group.
>denying credit on the basis of race, color, religion, national origin, gender, marital status
Are any of these independent factors for greater (or lower) risk once other factors like income, education and conscientiousness are considered? If they aren't why do any lenders care and if they are then any lender is just going to use cruder proxies for these factors disadvantaging more people.
It's going to be interesting to see how these lenders show compliance with various aspects of the Fair Credit Reporting Act and Equal Credit Opportunity Act.
The latter, for instance, prohibits lenders from denying credit on the basis of race, color, religion, national origin, gender, marital status, etc. But it's possible some of the data points used by these lenders will turn out to be proxies for these attributes. There is already a lot of discussion around how lenders are using "big data" and the associated potential pitfalls[1][2].
[1] http://www.microfinancegateway.org/library/big-data-big-disa...
[2] https://www.aclu.org/blog/ftc-needs-make-sure-companies-aren...