For lenders, finding people and businesses asking to borrow money isn’t much of a challenge. Finding businesses that can pay it back — that’s another story.

For banks and alternative lenders that want to find potential customers, the key is accurate data. That’s easy when it comes to publicly traded companies. Their financial statements are audited, public, and quite often located in databases that are organized. The story is different for privately-held companies.

Unlike their publicly-traded counterparts, there’s no central database of the financials for private companies. In many cases, smaller private companies might not have audited financial statements. Finding lists of qualified potential borrowers in this environment has always been a challenge.

An alternative lender recently showed how Powerlytics data can not only find qualified borrowers by accurately predicting the revenue of small businesses, but the same data can be used to speed the application process.

Accurate income prediction

Prior to using Powerlytics data, the alternative lender turned to tools that banks and other lenders have relied on for decades to find new customers: Credit bureau data and a few other variables.

That data is often inaccurate, and it wasn’t made for customer acquisition. The lender set off to improve its customer targeting.

To start, the lender had a file of small business applicant data that had been verified by tax returns to use as a control group. The lender plugged its existing attributes into its revenue prediction model. Then it did the same thing with Powerlytics data. Results were reported in six gross monthly income tiers: $20,000 to $29,999; $30,000 to $39,999; $40,000 to $49,999; $50,000 to $59,000; $60,000 to $125,000; and companies with income above $125,000.

In every category, Powerlytics government sourced data, of anonymous financial statements for over 30 million for-profit businesses and tagged by industry sector, location, legal form, and number of employees, produced more accurate predictions of customer income than the lender’s traditional data set. In all, the alternative lender improved the accuracy of its predictions by an average of 20 percent.

But the benefits went beyond customer acquisition.

Accuracy begets speed in verification

Alternative lenders have been able to make significant inroads in the financial services world by emphasizing speed and user convenience. They pride themselves on their ability to make decisions quickly. Streamlined applications also help alternative lenders compete among themselves. And a borrower that hits a roadblock may decide to go somewhere else.

Because the Powerlytics data supports accurate income predictions, it also serves as an income verification tool that streamlines application processes. The alternative lender only needs to ask a borrower a few quick questions on a loan application to qualify the applicant. If those answers are close to what the income model predicted, the lender can move forward without asking for additional information.

A useful tool for other industries

Though this analysis was performed by an alternative lender, the results aren’t limited to that sector. Revenue prediction models could help financial services or insurance companies prospecting for new clients. They could be used by traditional lenders, or by companies looking to sell into an industry by targeting firms of a certain size.

In other words, revenue proxies can do much more than indicate whether a business can pay back a loan. It can also give you an idea of whether a business can afford your product or can be a viable member to be part of your supply chain.

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