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Credit union best practices for MBL systems

Sageworks
January 24, 2014
Read Time: 0 min

Business lending has more complex variables and carries more risks to credit unions than other types of lending, according to the National Credit Union Administration (NCUA). That’s why it’s important for credit unions to incorporate critical elements of a sound member business lending program, including having MBL data processing systems that can service the unique portfolio

Data systems designed for consumer lending generally lack the specifications to service an MBL portfolio, the NCUA has said. Credit union management and directors should be willing to make the capital investments needed for “systems capable of managing an MBL program,” the agency has said. 

Here are six best practices for MBL systems, as outlined by the NCUA:

1. Systems can organize data for loan files.

2. Systems are able to monitor credit relationships on an account-by-account basis and in the aggregate.

3. Systems have the ability to prompt periodic reviews in order to identify and cure deteriorating credits.

4. Systems can aggregate problem loans and watch lists based on the credit union’s internal risk-rating system.

5. Systems can track compliance with covenants specific to the loan.

6. Systems incorporate tickler systems that provide for timely tracking and follow-up on filings related to secured interests, updated financial statements and insurance information.

For information on managing other common areas of risk associated with member business lending, download the whitepaper, Member Business Lending Landscape: Managing Risk & Opportunity, or listen to the recent webinar, How to Manage Risk in Member Business Lending.

About the Author

Sageworks

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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