Using Application Analytics to Approve More Loans
August 2016

Veteran salespeople will tell you that while it’s never easy to close a deal, the hard part is getting someone to even consider making the deal in the first place.

Consider that nearly two-thirds of people who request information on a business won’t make a purchase for at least three months, and one-fifth will take more than a year to pull the trigger, according to Marketing Donut.

Or that, generally speaking, only 3% of your market is actively pursuing a purchase, and 56% aren’t at all interested.

So it doesn’t take a genius to realize that when a motivated buyer puts forth a good faith effort to do business, you’d better figure out a way to consummate that relationship if at all possible. Not that you can’t score surprising victories on some of those long shots, but the law of averages dictates your business success depends on consistently plucking the low-hanging fruit.

Why, then, do so many credit unions and banks leave money on the table by finding reasons to say “no” to relatively worthy would-be borrowers, instead of finding reasons to say “yes”?

Most often, it’s because they lack the credible data to make educated assessments about these consumers’ creditworthiness, which leads them to retract into a conservative posture.

Using loan application analytics empowers financial institutions to minimize credit risk. By analyzing trends and variances in loan applications, you can implement better underwriting criteria and more sound loan policies. This allows you to approve not only better loans but more loans — including higher-priced options for borrowers with less stellar credit — paving the way toward greater profitability.

Many variables can be scrutinized to explore opportunities or clamp down on losing propositions, including denial code, loan term, loan officer, collateral value, interest rate, credit score, applicant’s place of residence, or location of the loan origination.

Credit unions and banks can use application analytics to assess performance rates, plot the course of key statistical metrics, and run “what-if” scenarios on potential lending strategy shifts.

Real-life examples include:

Analyzing funded, approved and denied ratios by product type and credit score tier — and, if you do indirect lending, by dealership and how those ratios trend over time.

Viewing a disposition report by multiple segments. This report should compare approved, approved-funded, approved-not funded, denials, withdrawn and percent of total applications.

Running what-if scenarios that test how changes in credit scores, loan-to-value, and debt-to-income can be adjusted for better loans.

Running fair lending analysis to guard against disparate impact.

Application analytics provides a clear view of whether your financial institution optimally aligns underwriting policies with your lending goals, in both meanings of the word — philosophically and budgetary.

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