Credit Unions

Deep Dive: How CUs Can Close The AI Innovation Gap

Credit unions (CUs) have followed banks’ digital banking lead, churning out platforms, services and mobile apps for consumers to use speedy, convenient online services. However, CUs face an innovation gap when compared to big banks that are racing ahead and implementing technology to better evaluate risk, provide personalized insights and foster greater financial inclusion in their markets.

Experimentation with artificial intelligence (AI) and machine learning is widely expected at larger financial institutions (FIs), where 52 percent of surveyed executives stated that they will be regularly using one or both within the next two years. Only 15 percent of surveyed CU executives expected the same. FIs of all sizes and enthusiasm levels have steadily moved forward with AI and machine learning integrations into behind-the-scenes lending, as well as consumer-facing apps. The “move fast and show off AI” approach may work well for major banks, but is more difficult for CUs, as they have more hurdles to jump — given their smaller balance sheets and technology teams.

CU executives have cited several reasons for their lack of AI or machine learning innovation, including costs, data quality questions and security concerns. However, they need to start taking AI experimentation seriously to remain competitive with larger FIs, as well as with smaller FinTech firms and lenders coming to take a bite out of their member bases. Those that do wind up embracing AI will be able to provide personalized services to their users, which can contribute to a rise in overall financial health and customer satisfaction as these AI-driven insights take root.

CUs And AI Development Barriers

AI and machine learning both have a dizzying array of potential use cases and benefits, but that is precisely what is keeping some CUs from adopting the technology. A recent survey found that 20 percent of CU executives admitted that they had no idea where to start with machine learning — and the strong hype surrounding these technologies likely worsens that confusion.

The need to overcome that particular stumbling block is becoming more pressing for smaller institutions, as end customers begin to ask for wider support from their banking partners. Half of U.S. customers want to use online or mobile budgeting tools that can help track spending, according to 2018 research, and 44 percent of those same customers want apps that aggregate all their financial accounts and personal information. Traditional banks and CUs are also facing more financial product competition than ever before, especially as eCommerce and technology companies like Amazon, Facebook and Google try to enter the space.

Credit unions that have picked an AI integration starting point still have more barriers to pass. AI and machine learning tools rely entirely on data, and 31 percent of CU executive have worries over both the access and quality of the data they will have. More accurate lending risk models or personalized spending insights rely on AI having more data to crunch, explaining why a common finance need includes access to larger data volumes or precise customer behavior data. CUs must be able to feed their AI and machine learning models the steady information diet needed to properly function, often by tying their platforms to other third-party services and partners.

One of the most significant obstacles for credit unions can be overcome via these collaborations. AI and machine learning integration prices concern CUs, specifically the costs incurred if a lending infrastructure upgrade was necessary for these already expensive technologies. A survey found that 43 percent of CU officials believe that one of their largest adoption barriers is the complex, lengthy AI or machine learning integration process for their technological infrastructure. Partnering with a third-party technology provider could sharply curtail frustration, without forcing credit unions to completely overhaul their online systems at cost.

AI And Its Growing Necessity In Finance

The reality is that modern CUs have little choice but to find a way to adopt AI and machine learning into their platforms and services. Providing faster and more intuitive financial services means relying on automation at scale, ensuring that tailored AI solutions are key to competing.

These technologies are changing customers’ view of financial services, so credit unions need to alter their offerings in response. Those that succeed in adopting AI solutions that both fulfill and anticipate financial needs will be able to stake greater claims in today’s growing and competitive online banking world.

——————————–

Latest Insights:

Our data and analytics team has developed a number of creative methodologies and frameworks that measure and benchmark the innovation that’s reshaping the payments and commerce ecosystem. In the November 2019 AML/KYC Report, Zillow’s Justin Farris tells PYMNTS how the platform incorporates stringent authentication without making the onboarding and buying experiences too complex.

TRENDING RIGHT NOW