Why do lenders reject so many credit applications?

A look behind the scenes helps shed light on why lenders reject many good clients, and what dealers can do about it.

Some finance managers at dealerships are mystified when seemingly good financing applications are rejected, but in the hustle and bustle of trying to get the next deal closed, they often just move on and don’t give it much thought. 

 But there are lots of hidden opportunities for dealers in working rejected applications, and perhaps more importantly understanding why certain deals get rejected in the first place. 

 When dealers get an auto decline from a lender, they often get an “answer” that comes back in less than a minute that says: “Consumer does not fit the lender criteria.”

 With so little information to go on, it’s understandable why finance managers just move on. They often just send the deal to another lender, and if rejected, then to another lender, and eventually they just close the file.

 Behind the scenes, lenders have their own internal scoring system. They have a view of the client’s past history and their own historical data not available to anyone else. So, in some cases, no one from outside their four walls has an idea of the actual score. For example, many banks are “allergic” to lending money to any former client who missed a payment with them as a lender. 

 But the vast majority of the reasons why lenders would decline a file are actually in the hands of dealers—they have the data. Lenders base 90 per cent of their decisions on the data provided by the dealership and on credit bureaus. Dealers are just not reading it to figure out if they’re gonna be declined or approved prior to submitting.

There are lots of hidden opportunities for dealers in working rejected applications, and perhaps more importantly understanding why certain deals get rejected in the first place.

Dealers could spot a lot of issues by pulling the client’s credit bureau. For a full picture they should examine both Equifax and TransUnion reports.

They should also remember that when it comes to credit applications 60-65 per cent of all transactions are not even looked at by a human, and that means auto approvals and auto declines can contain mistakes. 

In one example, we had a client who had applied for financing while under bankruptcy. When we pulled the file, we could see a vehicle repossession occurring nine months after the bankruptcy, but in fact it was included in the bankruptcy. This triggered a rejection, but it was a case of bad timing—not another issue on the credit file. 

In this case, the issue was flagged by Lucy, our automated system that uses predictive AI to help dealerships navigate prime and non-prime finance applications. Lucy flagged it and identified there was a Ford Motor repo, right after the bankruptcy that didn’t add up on the timeline. Also, based on a historical timeline, the client used to always pay on time, so something wasn’t adding up. The dealership just needed to get the necessary documents from the bankruptcy trustee, and then resubmit to the lender to get approval. When presented with the information, the lender reversed their decision.

In this case, a skilled F&I manager might have been able to spot the problem, but the reality is that with the volume of transactions and submissions, and the variability in the criteria for each specific lender, prime and non-prime, an automated artificial intelligence platform like Lucy can act as a trusted companion to your business office.

Lucy does a good job at talking to automated financial systems, because, well, it takes one to know one. When she assesses a client file, she will return the lenders that match the lender programs and who will “green light” the deal, the “red lights,” which won’t approve it, and the “yellow lights” that represent lenders in the gray zone. 

Because Lucy’s algorithms consider each lender’s financing requirements, she will provide insights to the F&I manager about which issues are causing the uncertainty. In some cases Lucy will recommend reducing the vehicle value or adding cash into the transaction to minimize the risk so the lender will say yes. 

In effect, Lucy knows her lender friends well, and knows how to navigate yes, no, and maybe. She can also do this in a matter of seconds, and has no emotional attachment to the client’s hopes of acquiring a particular vehicle. 

So, while she will provide the information needed to present a credit application in the best possible light, she won’t lose any sleep if a lender rejects or accepts a submission. She is, after all, a machine who learns!

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