As credit unions plan their budgets for 2025, there is an increasing focus on innovation, and artificial intelligence is playing a pivotal role in shaping this evolution. However, while AI solutions are proving extremely successful in areas like fraud detection and risk management, customer engagement is an emerging area where credit unions must approach AI implementation with caution, ensuring compliance and ethical responsibility.

Not surprisingly, AI is top of mind for many credit unions. In fact, Jack Henry’s 2024 report revealed that relative to banks, credit unions give outsized priority to investments in AI. Credit unions are also allocating more of their budgets to data analytics and automation.

Moreover, American Banker's annual Best Credit Unions to Work For survey revealed that nearly one in five credit unions are using or plan to implement chatbots or virtual assistants. Fifteen percent said they are exploring AI-driven loan decisioning and underwriting technology.

AI presents exciting opportunities for credit unions, particularly to enhance financial wellness for credit union members, but it must be approached correctly. Credit unions can offer hyper-personalized advice and automate consumer financial wellness end-to-end. Based on the rules and settings set by the credit union, AI can provide real-time advice on products such as HELOCs, credit cards and consolidation loans, improving member engagement and retention.

Ultimately, AI can help credit unions live up to their long-standing reputation of being member centric. However, as they explore these possibilities, they must focus on a few key areas:
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Regulatory Compliance: Ensuring Transparency and Accountability

As credit unions move toward adopting AI-driven solutions, regulatory compliance must be a top priority. The financial industry operates under stringent regulations designed to protect consumers, and AI tools need to function within this legal framework. Solutions that provide real-time, personalized financial advice must be built with transparency in mind – members should always know how decisions are made and how their data is being used. In fact, this is expected. CFPB Director Rohit Chopra said in guidance issued last year on AI that “creditors must be able to specifically explain their reasons for denial. There is no special exemption for artificial intelligence.”

Furthermore, AI-powered platforms should comply with the Fair Lending Act, Equal Credit Opportunity Act and other relevant consumer protection laws. Non-compliance could attract scrutiny from regulators, and in worst-case scenarios, it may lead to fines or damage to a credit union’s reputation.

It’s also important to note that while some credit unions assume that fair lending enforcement is not a concern for them, the NCUA has indicated otherwise. In 2021, for instance, the NCUA identified consumer compliance violations at nearly 15% of federal credit unions examined.

By ensuring that AI solutions are designed with compliance at the core, credit unions can leverage these advanced tools to benefit members while maintaining accountability and regulatory approval.
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Bias Mitigation: Promoting Fairness and Inclusivity

One of the most critical challenges in AI development is the potential for unintended biases in decision-making. Without careful design and oversight, AI algorithms may perpetuate or even exacerbate inequalities, particularly in areas like loan approvals, financial product recommendations and credit assessments.

For credit unions, who often pride themselves on serving diverse communities and promoting financial inclusion, this risk is particularly concerning. Bias in AI can alienate members and damage trust. But when done correctly, AI can promote fairness by delivering personalized advice based on individual financial situations rather than broad demographic assumptions.

Credit unions must work to ensure their AI tools are blind to biases related to race, gender or socioeconomic background, while remaining empathetic to individual financial needs. This can be achieved through rigorous testing, ongoing monitoring and the integration of ethical frameworks in AI system design. Ultimately, AI should serve as a tool to enhance, rather than limit, inclusivity.
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Ethical Use: Building Trust Through Explainability and Empathy

For AI to be effective, members must trust it. This trust is built on clear, understandable decision-making processes and ethical use of data. Credit unions are uniquely positioned to foster this trust by ensuring that AI systems are not just compliant but also transparent and empathetic.

Members should be able to understand how AI-derived recommendations are made and feel confident that their data is being used responsibly. Again, this is also required, as noted earlier. Explainability is critical – members should know why a specific product, such as a HELOC or consolidation loan, is being recommended to them based on their financial history.

Additionally, AI tools can enhance member relationships by offering proactive, empathetic advice that helps members navigate complex financial challenges. For instance, if a member is struggling with debt, AI can provide real-time strategies for improvement, helping them build their financial health rather than simply denying them access to products. By prioritizing ethical use, credit unions can ensure that AI serves not only as a tool for automation but as a trusted advisor that aligns with their core values of member care and community support.

Through rigorous compliance, bias mitigation and ethical use, credit unions can harness AI’s power to deliver innovative financial solutions while maintaining their commitment to fairness, transparency and member trust. As AI adoption grows, so too must credit unions' dedication to using these tools responsibly, ensuring a future where both innovation and ethical standards thrive.