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Commercial Lending Software: Buyer’s Guide

December 22, 2015
Read Time: 0 min

Depending on the size of your bank or credit union, your credit department may be set up in different team structures. It might even be just a few people with shared responsibility.

If your institution underwrites and reviews enough loans annually, it’s likely that you have adopted some type of spreading, ratio analysis or global software designed to help you identify and measure credit risk in potential and existing borrowers. For the sake of this post, however, we specifically look at software intended for commercial loans.

This could include lending software used for C&I, CRE, Agricultural, SBA-backed or even Member Business (MBL) loans. The common characteristics would include complex entities with multiple borrowers, guarantors and real estate properties in addition to commingled debts and income. For these types of loans, here are some recommended criteria to consider during due diligence or vendor comparisons for commercial lending software:

Will the lending software improve the borrower experience?

For many SMB borrowers, the application process for credit can be tedious in the extreme. If your commercial lending solution leverages an online loan application, online document storage, and automated loan decisioning, you can make the application process easier for the borrower, and get back to them with an answer sooner.

Will it fit with your credit team’s existing workflow?

If multiple people are in charge of spreading or analyzing commercial loans, can they all access the lending software from their workstation or location? If staff members are used to creating credit memorandums in Microsoft Word, does the platform you’re considering fit into that plan? Or better yet, automatically create the memos so your staff just has to edit? This may not be the most important criterion, but it will increase adoption if the software doesn’t require a major procedural change.

Does the software perform calculations the same way your bank currently does?

There may be subtleties in the way debt service or other ratios are calculated, and the commercial lending software your institution chooses should accommodate the institution’s specifics.

Will the commercial lending solution help you calculate global risk?

If you’re changing software, you do not want to go backward in the way it helps your lenders account for global credit risk. Instead, confirm that the software you’re evaluating is able to help your institution complete double counting adjustments accurately to arrive at a global cash flow analysis.

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Does the solution provide a forward-looking measure of credit risk?

With the increasing focus on future loss, your commercial credit software should also help your institution be more proactive in identifying future risk with business borrowers. Can the solution build projections to estimate future financial performance? Or, stress test a few different loan proposals to assess the impact it will have on the borrower’s ability to pay?

Can the solution benchmark your prospective borrower?

Whether you are new to lending in that industry or a seasoned veteran, to understand a business’s performance often requires looking at how the business compares to peer companies. Your institution undoubtedly has some unique characteristics to evaluate, but hopefully, this list can serve as a starting point for conversations about automation through commercial credit software. These suggestions focus solely on the software, but your institution will likely want to dig deep into limitations in the Service Level Agreement (SLA), customer support provided by the vendor, availability of updates/enhancements, existing customer base, and references, etc.

Good luck with the search!

About the Author


Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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