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How credit unions are using AI to enhance lending

Author: Scienaptic Research

Delivering exceptional member experiences and supporting their members is an ultimate goal for credit unions. They strive to enrich their communities by saying 'yes' to more members and establishing personal connections. We witnessed how several credit unions reached out to their members during the pandemic to offer help and expertise. They won over the brand loyalty from their members and added more revenue throughout the year. Time and again, the credit unions proved that member service is their topmost priority and focus. At the center of this approach, over the last few years, is the need for digitization. Members now expect more flexibility and agility in their transactions. Moreover, the pandemic has fast-tracked this need to be digital, personalized, relevant, and offer more.

In 2020, every credit union was challenged by significant margin compression and a prolonged low interest-rate environment. These pressures adversely affected the earnings, and in the process, some credit unions suffered a hit while others exceeded earnings and growth rates in multiples. These bottom-line increases had less to do with the pandemic and more with a calculative focus on technological investments, changing business strategies, and putting priorities in place for the next few years. Credit unions with a head start focused on innovative digital strategies, real-time capabilities, and agile response mechanisms found it easier to keep up with the changing market dynamics. Moreover, as the pandemic began, many credit unions found larger liquidity that needed to be utilized, which naturally led to the question - How do we lend more? How do we onboard more members? In this safe-distance environment, how do we make it all digital and appeal more? Naturally, keeping up with the large banks with big bucks was a challenge for many credit unions; however, the smarter ones researched, regrouped, and initiated their artificial intelligence journey soon after. The low-to-no-code AI-driven underwriting platforms are becoming popular amongst credit unions and allow them to lend more, help the underserved members, and allocate more attention to the complex borrower files.

For years it was a common misconception that Artificial Intelligence would remove the human aspect from the business. But, in reality, AI is an opportunity to deepen those already-strong member relationships and say "yes" a lot more. Artificial Intelligence can automate and improve services delivered to members while improving the bottom line. Implementing Artificial Intelligence allows the credit unions to:

  • Offer a new service to a member at an opportune time.

  • Reduce the credit losses by detecting delinquencies earlier

  • Provide an even more excellent customer service experience when a member has an issue

  • Deliver the convenience members seek and delight your customers

  • Leverage customer data across the organizations to improve real-time, easier detection of potential frauds, and reduce false positives

Post-pandemic, these are the experiences members are asking for, and Credit Unions must provide to remain competitive and relevant in today's environment. The AI's ability to increase a credit union's engagement with its members begins with utilizing data, incorporating a member's behavior, patterns, and financial history to produce enhanced insights. Artificial Intelligence recognizes when a particular service/offer is relevant to a member and triggers that offer, e.g., personal credit, savings, loans, insurance, and a home mortgage. In a true sense, AI can revolutionize a credit union's entire ecosystem and enable larger wallet share.

How can AI help CUs in their lending business?

The AI-underwriting or credit-decisioning engines claim to discover more lending opportunities from the credit union's very own borrowing pool. These efficiently use more information when making loan decisions than just the FICO scores. These platforms create ML engines that verify the borrowers with an evident ability to repay or default so that the straightforward loan cases can be approved or rejected instantly. That leaves the credit unions with the time to focus on the underserved members or those who would have otherwise been declined after using the traditional underwriting methods. These additional approvals turn into higher earnings for the CUs and help their goal of serving its members.

For decades, credit scores were the prime source of approving various products, such as mortgages and auto loans. This approach was based on a limited set of historical data limiting the number of consumers eligible for a credit and reducing a credit union's lending opportunities. With AI-powered models, more comprehensive and complete information is used through entirely unstructured data, such as current financial health, education, future employability, macroeconomic data, and projected earnings to decide a consumer's credit potential. Members with lower FICOs but who may perform equivalent to those with higher FICOs or, simply put - Hundreds or thousands of borrowers with scores less than 650 could be the possible lending opportunities that a CU can unlock with AI-based underwriting. In 2021, about 125M CU members had 1.9 trillion deposits, and only 1.2 trillion worth of loans had been approved. A clear opportunity to lend more and help hundreds of millions of members. (NCUA)

AI and AI-driven underwriting platforms don't differentiate between the size of credit unions and can be an excellent fit for asset sizes. Recently we see, e.g., a $45MN asset size credit union and a $40 BN credit union using the same AI-underwriting platform customized for their requirement, for, say, auto loans vs. residential loans. With time, many big and small credit unions are getting introduced to the power of AI and its underwriting capabilities and are coming onboard.

How can CUs start on their AI journey?

There is no perfect answer or a strategy to go about this, but many credit unions who are now a few years to a few months old in their AI journey have shared some insights from their journeys. Some of them are,

  • Credit union leaders from all asset sizes believe it is essential to research what AI can do and how it operates. It always makes sense to understand the technological ecosystem in which AI operates, as it also directs us to the integrations or systems it may require.

  • The following recommendation is to brainstorm and study the goals your credit union would want to achieve by implementing AI - Is it smooth customer onboarding? Or enhancing the customer service? Or increasing your member base?

  • Some leaders shared that they first did to get buy-in - from their boards, investors, and key employees. They added this transformational decision into their strategic planning for the next year or three to ensure no hindrance in implementation internally. Getting buy-in across hierarchy is extremely important for a notable change such as this.

  • One of the leaders mentioned that it is equally important to finding the right technology implementation partner. Many credit unions have faced challenges in finding the right partner that aligns with their business ecosystem. It is essential to find a tech partner compatible with your current technology readiness, whether people or the platform. This aspect has delayed the AI journeys of many credit unions by months or years.

  • The last bit that is independent yet significantly correlated to initiating the AI journey is - building a data warehouse. Data is the backbone of any AI-ML platform, and credit unions should focus on maintaining their data and creating a data pool that can be used to feed into the AI-powered platforms.

The top three challenges companies face when considering the implementation of AI are staff skills (56%), the fear of the unknown (42%), and finding a starting point (26%). AI has opened many possibilities to gain a competitive edge, streamline organizational transformation and put large volumes of underutilized member data to create meaningful and personalized experiences for members. The current use of AI in credit decisions and lifecycle management makes preparing a comprehensive AI Roadmap to enhance the member experience. In the post-vaccine world, AI-based decisioning can leverage growth opportunities by improving access to credit while helping underserved members get back on their feet.

The strategic planning season presents an opportunity for all credit unions to identify and incorporate new imperatives that can help mitigate risks and streamline critical processes. This leadership webinar aims to offer insider tips on why AI-based credit decisioning should be a part of strategic planning for all credit unions. Click here to watch the webinar video.

Watch the full webinar here:

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