FinovateFall 2025: Financial Institutions Find Real Value with AI Beyond the Hype

FinovateFall 2025: Beyond the Hype - How Financial Firms Are Finding Real Value with AI

This week at FinovateFall in New York, industry experts gathered to discuss how financial services are moving past the initial excitement around AI to focus on practical implementations that deliver real business value.

AI adoption is accelerating across banks and credit unions, from chatbots to advanced predictive analytics. While many announcements highlight new AI initiatives and partnerships, financial leaders are now asking more pointed questions: Where can AI drive revenue growth or cut costs? How mature are these technologies in everyday use? And how can firms distinguish genuine innovation from hype?

Current State of AI Adoption

Panelists agreed that AI in financial services is still in its early phase, but progress is tangible. Sam Kilmer from Cornerstone Advisors noted that 30-35% of US banks and credit unions have generative AI projects underway. This confirms AI's presence beyond mere hype.

Fred Campbell from TRAC.vc compared this stage to the internet in 1995—still early, with sceptics, but poised for major impact. Assaf Baciu of Persado pointed out that while AI shows promise in areas like marketing and customer service, effective implementation requires more effort than many expected. Human oversight remains critical to move AI applications from "almost good enough" to truly enterprise-ready.

Build vs. Buy: Choosing the Right Approach

Deciding whether to develop AI systems internally or partner with fintech providers was a key topic. Baciu recommended starting small with a specialised partner to achieve consistent outcomes, as mastering AI requires significant expertise.

Credit unions, represented by Sherry Wu from University of Michigan Credit Union, often lack the resources to build in-house solutions. They prefer fintech partnerships but stress the need for collaboration and customisation rather than turnkey solutions. Wu advised financial firms to begin with a clear strategy focused on member needs, then decide which use cases to tackle first and whether to build or buy accordingly.

Key Use Cases Driving AI Value

• Risk and Compliance: AI is first making headway in fraud detection and regulatory compliance, areas critical for reducing losses and avoiding penalties.
• Cost Reduction: Automating routine processes is helping institutions improve operational efficiency and reduce headcount growth.
• Revenue Growth: The real differentiator will be those who use AI to generate new income streams or enhance customer engagement.

Wu emphasized AI's role in personalisation, using data to anticipate member needs and deliver tailored financial advice. Campbell highlighted operational efficiency gains, sharing how AI enables his firm to handle far more investments per employee compared to competitors.

He also raised concerns about AI-powered fraud, noting that combating increasingly sophisticated threats will require leveraging AI on the defense side as well.

Baciu shared that AI-driven communication can boost conversion rates by about 31%, improving outcomes in lending, deposits, and other services. Contact centres also represent a significant opportunity to improve customer service through AI-assisted interactions while managing costs.

Looking Ahead

Financial institutions are transitioning from AI hype to practical applications that deliver measurable results. Marketing, fraud detection, and operational efficiency are already benefiting from AI tools. The next step will be adapting to customers who themselves use AI agents to manage finances.

The firms that balance technology with human oversight and focus on clear customer priorities will be best positioned to thrive in an AI-influenced financial landscape.

For those interested in deepening their AI skills relevant to finance, exploring targeted courses can be useful. Resources like Complete AI Training’s finance-specific AI tools offer practical learning pathways.