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AI-based Lending Decisions to Provide Enhanced Member Experience

Credit unions have long taken pride in building relationships that prioritize people over products, and that has set them apart from their competitors. Their intent to give the profits back to members and their willingness to always be there for those members has earned true brand loyalty. However, expectations have changed in the past few years owing to the digitization and the disruption caused in the physical world by COVID-19. The pandemic has raised the bar for prioritizing member service. More members are now navigating online banking to make loan payments, accessing a check, or applying for a loan. Customers all around the world expect uninterrupted and accessible guidance and support through call centers and online contact forms.

Despite the hurried adoption of digital solutions to meet the demands of members in the new normal, a recent study by reveals that 43% and 29% of Gen Z and Millennials showed a willingness to leave their CUs. Additionally, 53% CU members reported using credit products from their CU’s competitors. So a member may maintain a savings account at a local credit union branch because their staff is nice, but still use a competitor for a better loan offer. The point is that everyone defines good service differently. Today member experience is a lot more than friendliness.

How does one measure member experience?

Researchers have historically used metrics such as customer satisfaction index and net promoter score to measure the credit union member experience. In spite of its usefulness for determining brand loyalty, the net promoter score doesn't necessarily correlate with market share growth. It is possible for a credit union to have negative membership growth and still have a high net promoter score, since members who remain are loyal, but those who have left find it difficult to do business with the credit union.

The Filene Research Institute’s white paper, “Member Effort Benchmarking”, defines member effort as “the amount of effort a member has to expend to resolve an issue, open an account, get a loan, join the credit union or accomplish any other goal ... across all channels including digital channels.” Another study published in the Harvard Business Review in 2010, "Stop Trying to Delight Your Customers," reveals that ease of doing business trumps everything else as a measure of customer loyalty.

Now, each credit union defines member experience differently too, but regardless of that it can be broken down into three parts, i.e. offering competitive products and financial expertise, ease of doing business and creating a memorable experience.

How do AI based loan decisions hit the sweet spot of member experience?

Faster Loan Decisions

AI-based lending decisions analyze real-time data of the members to create prompt credit decisions for all types of loans. AI-powered models utilize data comprising a member's behavior, patterns, and financial history and don't entirely weigh in the FICO scores but treat them as another input. The cutting-edge credit decisioning offers greater insight into borrowers' willingness to pay. The compilation and assessment of these separate data points are now possible in seconds as compared to the time-consuming and labor-intensive work that an underwriter would go through for a loan. The turn-around time in gaining credit decisions and processing loans has drastically decreased due to the ease of decision-making through AI models, becoming the most loved factor by the members.

AI helps you make personalized loan decisions for each member

Credit Unions may use a blanket approach of putting members before products but what doesn’t work when it comes to engaging members is treating all members the same. That is, talking about products or services without knowing what is relevant to their needs. AI based credit decisions help you know your members’ needs before they know it. A credit union could use early warning signals for payments, pre-approve loans, and offer customized loan products that best suit your member’s needs.

Access to credit for the underserved

Scienaptic, a leading credit decisioning platform provider adopted by several credit unions across the United States has significantly increased loan approval rates for them. Their industry leading AI powered credit decisioning platform is helping credit unions approve deserving applicants who might otherwise have been denied a loan due to low FICO scores. "Traditional systems that predominantly rely on FICO or vantage scores to evaluate a borrower's creditworthiness lead to missed opportunities for the CUs and most importantly for applicants who deserve credit." cited Pankaj Jain, President, Scienaptic AI. "Well, my experience with credit unions of all sizes and expertise has assured me that this misguided reliance on credit scores alone restricts the economic activities of consumers and reduces the CU's chance to earn profits."

Moreover, underwriting teams are now able to delegate a substantial part of the approvals or denials to the algorithm. That leaves the underwriting personnel to dedicate their efforts to more complex loan cases while making underwriting more efficient, taking member relationships to the next level.

Fewer Chances of Bias

Lending decisions for all financial institutions have long been riddled with human bias. AI-based credit decisions use millions of data points and self learning algorithms to assess a member’s loan request. This removes intentional and unintentional biases based on gender, race and ethnicity. AI based loan decisions can lead to greater financial inclusion of underserved members and businesses.

There is no doubt that the implementation of AI-based lending decisions elevates the quality of member experience. Credit Unions are no longer competing with neighborhood banks and other credit unions. They are competing with the ‘instant clicks’ of Apple, Amazon and Google and must create experiences that stand out.

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