Traditionally, the loan underwriting process has been a slow, cumbersome affair. But now, lenders are increasingly adopting automated underwriting systems to streamline the process and expedite loan decisions.
Traditionally, the loan underwriting process has been slow and clunky with multiple data sources and lots of paperwork to sort through. Lately, more and more lenders have adopted automated underwriting systems, including Freddie Mac, which announced automated underwriting capabilities for mortgage lenders in 2022. While mortgage lenders still haven’t adopted a fully digital end-to-end solution, they have benefited from decreased costs and faster processing times.
Automated underwriting can be equally beneficial for SMB lenders.
At the most basic level, underwriting automation entails using technology to collect and analyze information about a borrower to make a loan decision.
An automated underwriting system will verify firmographic and demographic information, along with third-party data, including identity verification, tax documents, know-your-business compliance checks, credit bureau checks, and account verification.
Once the data has been collected and analyzed, the system can finalize approval of the loan within 15 seconds.
Financial institutions may choose to implement automated underwriting in a number of ways including:
The benefits of automation in banking are vast, including:
Banks pride themselves on having well-documented policies. In fact, many banks have taken important strides to sharply reduce—and even eliminate—the elements of subjectivity in decision-making. However, in practice, lenders often have exceptions. But what if, rather than designing a process to accommodate the exceptions, we designed a process to optimize for the vast majority of applications that can be accurately measured and adhere to a written policy?
Of course, like the adoption of any new technology, automated underwriting has produced a debate around when/if a machine truly is a better option than a human. A human, for example, can better assess nuance than a machine. Does this then give the human the advantage in better predicting outcomes? In actuality, studies have found that machines are better able to assess risk for borrowers with lower credit scores or a prior history of bankruptcy.
Users adopting the technology also need to trust that the decision engine is accurately assessing risk profiles. To help alleviate those concerns, underwriting systems can be adjusted to match the bank’s risk profile.
Automation in banking has a much bigger role beyond underwriting. It can be used to pre-qualify borrowers and market to them, creating a much more attractive offer for the borrower and better ROI for the lender’s marketing and loan officer teams.
In this scenario, the loan origination system pulls in transaction data from the bank’s system and third-party sources, classifies those transactions, tabulates results to develop a profile, and then runs that profile against a predetermined transaction policy. Based on the results, the bank can then send a pre-qualification offer to the customer.
There is a huge opportunity for the lending industry to become more efficient, reduce risk, and capitalize on the underserved small business market via automated underwriting. Banks that adopt this technology early will have a key competitive advantage over other institutions as automation in banking becomes more mainstream.
Are you interested in lending automation? Learn more in this ebook, Automated Lending: A Mandatory Upgrade.
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