Developing an AI Strategy: A Roadmap for Credit Unions (2024)

The following is an article written by Trellance’s Chief Product Officer of Analytics, Paolo Teotino. It originally appeared on CUInsight.com.

AI adoption in the financial sector has seen remarkablegrowth. According to a 2023 survey by Deloitte, 70% of financial services firms have integrated AI into their operations, and 90% of those firms report measurable benefits from their AI initiatives. GenAI, in particular, has revolutionized the way financial institutions operate, creating predictive models, automating processes and personalizing customer interactions.

Several financial institutions have successfully integrated AI to enhance their operations. JPMorgan Chase, for instance, uses its COiN (Contract Intelligence) platform to analyze legal documents, saving over 360,000 hours of manual work annually. Bank of America has Erica, an AI-driven virtual assistant, that helps customers with everything from transaction searches to financial advice. USAA utilizes AI to detect and prevent fraud, ensuring secure and seamless transactions for its members.

The opportunities AI offers can be transformative for credit unions as well. One of the most significant advantages is the enhancement of member experiences. AI can personalize interactions by analyzing transaction data and member behavior. For example, chatbots and virtual assistants can provide 24/7 support, answering queries and offering limited financial advice, leading to increased member satisfaction. Imagine an AI-powered employee assistant in a credit union. This assistant could retrieve knowledge from vast databases, supporting customer care representatives or branch managers in real-time. Whether it’s answering a member’s query about loan options, providing insights into financial products or assisting with complex problem-solving, this AI assistant could significantly enhance the efficiency and effectiveness of credit union staff.

The potential of AI doesn’t stop there; here are additional use cases where AI can revolutionize credit union operations.

Personalized financial planning:AI can analyze a member’s financial history and goals to offer tailored financial advice and product recommendations. This can range from suggesting the best savings plans to providing investment advice, all personalized to the member’s unique circ*mstances. Morgan Stanley is a great example of a financial institution using an integrated OpenAI-powered chatbot to assist financial advisors by providing access to the firm’s extensive internal research and data, enhancing the accuracy and efficiency of financial advisory services.

Enhanced fraud detection and prevention:AI systems can continuously learn and adapt to new fraudulent activities by analyzing patterns and anomalies in data. This proactive approach can significantly reduce fraud incidents and protect members’ assets. JPMorgan Chase for example, uses email pattern analysis to identify potential threats. Through additional AI programs, they estimate they will drive $1.5 billion in value through AI by the end of the year.

Predictive member insights:AI can alsopredict member needsand behaviors by analyzing historical data and current trends. For example, it can identify members who might be interested in refinancing their mortgages and proactively offer them personalized refinancing options.

Streamlined loan processing:AI can automate and expedite the loan approval process by quickly assessing credit health and risk profiles based on extensive data analysis. This not only speeds up the process but also enhances accuracy in decision-making. Several credit unions like Suncoast, VyStar and many others use AI solutions to increase automation and lending efficiencies while managing risks.

Dynamic member engagement:By analyzing member interactions and feedback, AI can help credit unions develop more effective engagement strategies. This includes personalized communication, targeted marketing campaigns and improved service offerings tailored to member preferences. MSU FCU and Magnifi CU are examples of financial institutions actively using AI-driven next best product models to identify the ideal target audience for product recommendations aimed to increased member engagement and satisfaction.

So, how can credit unions develop a successful AI strategy?

To develop an AI strategy a credit union should start by defining clear, measurable goals such as improving customer service or enhancing fraud detection. Then, you should assess your current technology and staff capabilities to identify if any gaps are present (and they will be present). Third, you should ensure that your data is clean, consistent and securely stored, while adhering to privacy regulations like GDPR or CCPA.

Once these preliminary but fundamental steps are completed, you can start scouting for available AI tools that align with your objectives, which should include evaluating available third-party vendors. A good rule of thumb is to begin with a small-scale pilot program to test AI solutions, gathering feedback and making necessary adjustments by establish procedures to monitor AI performance. Once successful, refine and scale your AI integration across the organization. Investing in talent and training is also a crucial component for the success of your AI initiatives. Building an AI-ready workforce involves both upskilling current employees and potentially hiring new ones with the necessary expertise. Train your staff on the new AI tools and encourage continuous learning. You should foster a culture of innovation that embraces change and experimentation.

Lastly, don’t forget about your credit union’s most important asset: your members. Put in place a program to communicate with your members about how AI improves services and safeguards their data and create feedback channels for ongoing improvement. Your members should embrace the use of AI and not be concerned about it. At the end, don’t we all like when Amazon suggests us our next recommended purchase?