Interface.ai Launches Agentic AI Tool for Credit Unions

The company’s new offering, Smart Collections, was unveiled on Thursday, February 19. This tool utilizes agentic artificial intelligence (AI) to assist community finance institutions in addressing delinquent accounts sooner, preventing them from escalating and incurring higher recovery costs.

“Early-stage delinquency has become a focal point in our industry,” stated interface in a recent news release. “In the third quarter of 2025, credit unions reported a total delinquency rate of 95 basis points, while consumer credit data revealed that delinquencies of 30–59 days past due climbed to 1.13% in September 2025, nearing pre-pandemic figures. This highlights the importance of timely engagement and effective outreach.”

Given this context, collections managers at community banks and credit unions are feeling the pressure to achieve greater efficiency while maintaining members’ trust. Smart Collections is designed to meet this challenge with a system of “coordinated automated outreach and compliance-by-design.”

The tool is available now for pilot testing and phased rollouts, starting with early delinquency cases related to the consumer products most frequently associated with credit unions and community banks.

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“Community banks and credit unions excel in building relationships. However, early delinquency periods are moments where these relationships are put to the test—speed, empathy, and precision are crucial,” stated Srinivas Njay, the Founder and CEO of interface.ai. “Smart Collections offers institutions a enhanced capability: an agent that can operate across various channels, adhere to institution-specific guidelines, and consistently achieve resolutions—all while preserving member trust and ensuring a comprehensive audit trail.”

Recent research from PYMNTS Intelligence indicates that while interest in AI is pervasive among credit unions (CUs), many are still in the early or selective stages of implementation.

A small fraction have widely deployed AI throughout their organizations, creating a widening gap between member expectations and the services institutions can reliably offer. Interestingly, this gap is not due to a reluctance to adopt AI technology.

“Rather, the adoption of AI falters when essential conditions are missing,” explains PYMNTS. “Issues related to data quality, governance, and system connectivity are the primary obstacles preventing pilots from evolving into full-scale capabilities.”

Across the industry, credit unions acknowledge the need for improvement, with nearly half of their leaders reporting limited familiarity with AI applications, while only 17% describe themselves as very familiar. Approximately 42% indicate they have implemented AI in specific operational areas, yet a mere 8% utilize it across multiple organizational segments.

“This discrepancy emphasizes that experimentation has surpassed actual readiness,” concluded PYMNTS.

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