Interface.ai Debuts Agentic AI Collection Tool For Credit Unions

Community bank and credit union technology company interface.ai, has debuted a new collection tool.

The company’s Smart Collections offering, announced Thursday (Feb. 19), uses agentic artificial intelligence (AI) to help community financial institutions address delinquency earlier, before accounts proceed to later stages and recovery costs increase.

“Early-stage delinquency is becoming a sharper focus across the industry,” interface said in a news release. “Credit unions reported total delinquency of 95 basis points in Q3 2025, and consumer credit data showed 30–59 days past due delinquencies rising to 1.13% in September 2025, nearing pre-pandemic levels – underscoring why earlier engagement and higher-quality outreach matter now more than ever.”

Against this backdrop, the release added, collections leaders at community banks and credit unions face ongoing pressure to “do more with less” and still maintain member trust. Smart Collections, the company said, addresses this with “coordinated automated outreach and compliance-by-design.”

The tool is available now for pilots and phased rollouts, beginning with early delinquency use cases across consumer products most common to credit unions and community banks.

“Credit unions and community banks win on relationships. But early delinquency is where relationships are tested – and where speed, empathy, and precision matter most,” said Srinivas Njay, interface.ai‘s founder and CEO. “Smart Collections gives institutions a new kind of capability: an agent that can take action across channels, follow institution-defined policy guardrails, and consistently drive cures – while preserving member trust and creating a complete audit trail.”

Meanwhile, recent research from PYMNTS Intelligence found that while interest in AI is widespread among credit unions (CUs), most are still in early or selective stages of adoption.

Only a fraction have deployed AI widely across their organizations, creating a growing gap between what members expect and what institutions can reliably provide. However, the research has shown that this gap is not being driven by resistance to AI itself.

“Instead, AI stalls when the underlying conditions are not in place,” PYMNTS wrote. “Data quality, governance and system connectivity consistently emerge as the main constraints preventing pilots from becoming enterprise capabilities.”

Throughout the industry, CUs acknowledge they have a lot of work to do, with roughly half of their leaders saying they are only somewhat familiar with AI applications, and only 17% describing themselves as very familiar. Around 42% say they’ve deployed AI in specific areas of operations, but only 8% use it across multiple parts of their organization.

“That imbalance highlights how experimentation has outpaced readiness,” PYMNTS added.

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