Part 1: Loan Underwriting

The first piece we will be looking at is loan underwriting. When a consumer applies for a loan — e.g. a mortgage, the bank is primarily trying to assess the likelihood or probability that the consumer will fulfill his obligation and not default on the loan.

Traditionally, lenders have used a tried and test formula, based on the applicants credit score or ‘worthiness’, available collateral, and his capacity to repay the loan.

More recently, lenders have started augmenting this traditional data with additional informative features — including untraditional metrics such as email age, email domain strength, and social media activity — in extreme cases, models may even take into account what browser the consumer is using to complete his online application.

This wave of data augmentation in the field of consumer lending has been observed with SME lenders in developed markets and consumer lenders in emerging markets, where credit scores may not be readily available.

Once an overall assessment of an application is complete, including data verification, credit assessment, etc. the application is approved or rejected.

In leading financial markets, we have seen an increased level of automation of this step. Whereby a portion of the application funnel is automated (i.e. approved or rejected algorithmically) and a significant percentage is then routed to manual inspection.

So even today, an encouraging portion of certain lending funnels, are automated.