Maryland credit union speeding up credit decisioning, Federal Agencies “ok” with alternative data.
By Bill Bromback
Collecting, cleansing, organizing data. Then applying data science, artificial intelligence, and machine learning. This is the “secret sauce” that will help organizations drive innovation, remain competitive, and continue to achieve their operational and financial goals.
The focus of this Blog is to highlight two recent events that illustrate the need to collect, analyze, and utilize more data, as well as the power of artificial intelligence and machine learning in a real-world application.
FIRST FINANCIAL FCU – LEVERAGING AI / ML
I recently read a great article about how First Financial FCU is deploying AI and machine learning to speed up credit approvals. Their goal was to develop an AI / ML based underwriting model which mimicked their current underwriting practice as closely as possible, attempting to instantly approve every approvable loan, and have the underwriting team review applications that were denied before a final decision is made.1
Clearly, First Financial FCU’s initiative highlights and reinforces the application of AI / ML to enhance a vital business process, and as a result, creating an opportunity to improve their level of member service and productivity. I applaud their efforts. Looking towards the future, First Financial FCU is also considering the use of alternative data to enhance their consumer credit decisioning models.
ALTERNATIVE DATA & LENDING DECISIONS
Speaking of alternative data, the door to incorporating alternative data in credit decisioning models has been flung open on December 5, 2019, as several regulatory agencies (Federal Reserve Board, Federal Deposit Insurance Corp., Office of the Comptroller of the Currency, Consumer Financial Protection Bureau and the National Credit Union Administration) have publicly stated that “the use of certain alternative data may present no greater risks than data traditionally used in the credit evaluation process.”2
Taking a wider view of consumer data to make lending decisions may be exactly what the doctor ordered when trying to attract and expand credit union relationships with millennials (born between 1980 – 1996), a very important market segment. “By 2025, it’s projected that millennials will make up 75% of the U.S. workforce, and 46% of total U.S. income.”2
But millennials may be a challenge to support from a traditional credit decisioning perspective as they may have significant college debt and are changing or holding multiple jobs as they establish their careers. In that regard, the regulatory agencies noted that the use of alternate data “may be particularly beneficial for consumers who demonstrate reliable income patterns over time from a variety of sources rather than a single job.”2
If you’re looking to enhance operational and financial performance for your credit union by applying data science, artificial intelligence, or machine learning, please let us know, we’d be glad to help.
1 Rapport, Mark. “First Financial Creates In-House AI/ML Lending Experience”. CU Times. February 2020.
2 Pedersen, Brendan. “Regulators Give Wary Nod to Use of Alternative Data In Underwriting”. American Banker. December 2019.