Improving Credit Risk Insight

by ID Analytics

ID Analytics

Booking new customers isn’t easy. Negotiating price and evaluating an individual borrower’s loan quality as they sit and wait is a real challenge. Never mind the added pressure of complying with applicable regulations and the financial risks associated with approving the wrong customer.

So how do you approve more qualified customers, book more of your approvals and grow your business? Lenders are challenged to look at credit risk in a new way and factor in additional insight and information to have more confidence in their credit assessment of customers. This can open up opportunities when other lenders may just see risk. Following are four ways to leverage an expanded definition of risk.

Look for the “Fallen Angels”

The dollar value of originations to people with credit scores below 660 has roughly doubled since 2009, while originations for the other credit score groups increased by only about half.1 This growth in subprime loans means there are now potentially even more eligible borrowers that you need evaluate and determine if they are worth the risk for an auto loan. Unfortunately, credit bureaus focus on traditional loan data such as credit card, auto loan and mortgage history — which many not tell the whole story.

Traditional credit scores categorize sub-prime candidates as those with credit scores below 660, typically due to past transgressions or high revolving debt utilization,2 but that’s only part of the borrower’s life. They don’t provide any additional insight into other characteristics or financial relationships that could indicate a likeliness to pay, so risk managers may rely on the deal’s loan-to-value (LTV) and the applicant’s (DTI) to make a risk assessment, which can mean too many qualified customers slip through the cracks.

Let’s take a look at a hypothetical borrower, “Rachel.” She is a 30-year-old urban professional who just started her career, but has a subprime credit score. Using traditional data you can’t tell if she is a consistently poor borrower “Ruinous Rachel”, or “Rough Patch Rachel” who is on the path to once again becoming a good borrower and someone you want to approve.

Some customers may always be high-risk, however finding these “fallen angels” can be the holy grail of the subprime market because they offer new revenue streams. But how can a lender make a proper risk assessment that separates the “Rough Patch Rachel” borrowers, that are ready to rise, from the riskier borrowers?

Broaden Your Data Set

Not all data is created equal. Lenders can find success taking a broad, multifaceted approach to determining risk considering a combination of traditional and non-traditional data. While there is value in traditional credit scores, non-traditional data such as retail bank, wireless and utilities account information, as well as additional sub-prime information, including pay day and short term loans, is an important factor and can help to provide the richest credit risk assessment of an individual.

The combination of traditional credit information with this sub-prime and non-traditional information can be incredibly valuable. But it is challenging and time consuming for lenders to find this information through multiple sources. Having a single partner that knows both sides of the credit spectrum and can help provide this unique combination of data can truly be valuable to finding the fallen angels for increased bookings and business growth.

Be Mindful of Fair Lending

Fair lending requirements and compliance are a real concern for anyone in the auto space, particularly in the sub-prime market. Even the top indirect lenders need to be cautious with their business practices. When assessing tools a good solution should be able to help you to meet a range of regulatory requirements, reduce customer friction, and control operational costs.

The lender that can make the best, most competitive offer to a dealership at all credit levels is likely to win the most deals. Having an effective solution in place to help assess credit risk while not unnecessarily turning away borrowers or pricing ‘out-of-the-market’ can help win  deals without hurting your bottom line.




1. Federal Reserve Bank of New York, (August 2014) Quarterly Report on Household Debt and Credit, retrieved from

2. ComE-IN Background Definitions, FDIC (7/13/2007) retrieved from