When it comes to reviewing your collection queue, there are loans that show up expectedly and then there are those loans that looked perfect when they were booked and now, 4 months later, are awaiting charge off. Every loan is good the day it is booked, but anyone that has spent some time underwriting can attest that it is not surprising to see a loan that was booked right on the margin appear in the collection queue. These marginal loans are known to have high risk and the corresponding terms are reflective of this risk. But what about these loans that are booked in some of the highest credit tiers that are charged off in the first year? These are the loans that haunt us. Maybe this borrower had a 680 FICO and 2 tradelines that had all been paid as agreed but had less than 12 months of history. This loan gets booked and a few payments get made and then the borrower is gone. Collection attempts fail and it as if this person never existed, they’re a ghost. There is a chance that this identity was manipulated, a borrower trying to hide from their poor credit history, or the identity could have been entirely synthetic. These early payment defaults are extremely costly and can really skew the performance of a loan portfolio.

Early Payment Default – Borrowers that Ghost

So what can be done to mitigate the risk around early payment default as caused by risky identities that look good through the traditional underwriting lens?

In the data analytics business, we always joke that the answer is always: more data! In this case, it has more to do with the depth of the data over the breadth of the data. We need multiple vantage points into a single identity in order to see some of these ghosts. Let’s go back to the example of the borrower that had a 680 FICO and 2 tradelines that had all been paid as agreed but had less than 12 months of history. When we start to look at this identity from different vantage points, we realize that it does not have any assets, has no evidence of college attendance and was entirely invisible until the age of 35. These factors and many others point towards a synthetic identity. When it comes to a manipulated identity that is hiding from their poor credit history, we can see links from the manipulated identity to the real identity. Maybe this borrower is using their real name but someone else’s SSN and address. With deep data we can expose other SSNs and addresses linked to this consumer.

But there’s more…what if I told you that we could not only mitigate risk around early payment defaults but could do it while adding efficiency to your workflow?

By using an FCRA compliant alternative credit scoring solution like LexisNexis® RiskView™, you can gain the depth of data needed to elevate these new vantage points. Borrowers with synthetic and manipulated identities are either unscorable or score very low in RiskView. By positioning this FCRA solution first in the workflow, the lender can decline and issue adverse action based solely on credit risk factors. By auto declining these applications at the beginning of the workflow, the lender can keep these identities out of fraud resolution queues that require manual review and even more importantly, keep these applications from being booked. Those applications that pass the initial credit check through RiskView can then flow on through standard fraud and identity checks as well as any additional underwriting criteria that the lender has in place today.

Contact us to learn more about our holistic credit risk and fraud solution to help you gain efficiency on the front end and keep these loans off your charge off report on the backend.

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