Traditional lending practices have not fundamentally changed in decades. That's a problem.

As community lenders with close connections to your members, you know that credit scores and bank balances only tell part of the story. They might tell you when someone was late making a payment. But they don't tell you about a member's reputation or other meaningful statistics and data points that indicate they are a good borrower.

In short, they can't quantify what you qualitatively know about him – the value of his relationships within the community or the influence he has on his friends. They don't factor in who the person is behind the numbers.

The technology-driven credit analysis process has stripped out the personal element. It primarily relies on a few basic criteria such as the borrower's credit score, years in business and revenue, and less on the personal relationship businesses have with their lenders. Simply put, it doesn't leave much room or place emphasis on relationship-based loan decisions.

That's the bad news. The good? Perhaps ironically, technology can now be used to fix the very problem that it caused.

A burgeoning technical field is now introducing solutions that bring the personal insight technology eliminated back into the process.

Put another way, technology is making a very traditional form of community lending possible again by offering solutions that expand the credit process. Now, tech companies can take into account those personal reputations and help community lenders expand their sub $50,000 loan portfolios.

But how?

Today, people and companies live their lives online. They socialize, communicate, shop, share and network professionally. Companies can take that information, along with traditional data points, and analyze it for a more holistic view of who the applicant really is, beyond the numbers.

We harness all types of information, from Yelp reviews to macroeconomic data to the financial and inventory software systems of the business itself, and process the results. We use the lender's existing credit analysis, but also provide insight from these other data points via machine learning that could push borderline loans one way or the other.

The end results give community lenders more information to continue to make the robust, holistic assessments of creditworthiness that they are known for.

Using these new processes, you can decide whether to provide a loan based on more than just a credit score or account balance. You can make the decision based on the person, the idea, the passion and the potential. The technology allows you to once again assume your position as a community partner who fosters growth and instills trust.

You regain the personal without losing the efficiencies of technology because it's automated.

Lending to small businesses is approximately 17% below its pre-recession peak. The annual aggregate value of small business loans outstanding, as reported by the FDIC, has averaged approximately $650 billion a year since 2007 (peaking at $711.5 billion in 2008). And new loan originations by traditional lenders have been roughly a third of that, or $200 billion annually.

Without credit, small businesses, which traditionally create a majority of new jobs and account for half of the economic output, have been unable to grow, thus slowing the economic recovery.

But just like alternative lenders, credit unions, businesses and government can also use technology to solve the lending crisis.

Not only can technology give credit unions new sources of information and data, but because it is fully automated, it can do it at a competitive speed and vastly reduced cost.

That's good for community lenders and it's essential for the economy.

Trevor Dryer is CEO and co-founder of Mirador. He can be reached at 503-451-0518 or [email protected]