An ELO-rating model for Loan default prediction

ELO-rating is used to predict the performance of a sport player in a tournament or otherwise. This works as defining an ideal or an average model player and comparing the performance measures of all other players with this model player. This gives us an overview of how each player is better than each other indirectly.

This can be also work in finance. Although, no company is reportedly using this kind of technique, I believe we can create an ideal loan applicant by taking the average of all the loan applicants who have successfully repayed their loan. This will give us an idea of what an average non-defaulting loan applicant looks like and we can compare all the loan applicants with this model applicant.

This will not only reduce the need of credit report. This is a huge change in the cost per customer.

The best part about the process is that it will give out an ELO-rating or a new credit score along with a credit report with a CONFIDENCE INTERVAL. This means that when we are not very confident on our outcome (which is less than 7% of the time), we can work our way through by getting an actual credit report for the applicant. The potential of this task when tested on the demo data showed us that it can decrease the rate of defaulting from 8.5% to 1.8%