The company says its pre-seed/seed funding round, announced Monday (July 14), will allow Murphy to scale its product, expand its teams and expand internationally.
“Debt servicing has historically been a slow, fragmented, and highly analogue industry, relying on costly call centers and generic engagement methods that leave vast amounts of recoverable debt untouched or written off,” Murphy said in a news release provided to PYMNTS.
The company says it reimagines this process with the combination of artificial intelligence (AI) voice agents, “omnichannel outreach” and behavioral personalization.
The goal here is to provide an alternative to call centers and help companies recover debts faster, “while staying compliant and treating debtors with respect,” Murphy Co-founder and CEO Borja Sole said in the release.
According to the release, Murphy’s technology has been adopted by banks, telecommunications, utility and debt servicing companies around the world.
“Debt servicing is a $300+ billion global industry that is ripe for disruption. After reviewing countless verticals, this stood out as a space where AI can make a major impact,” said Jeppe Zink, partner at Northzone, which led the funding round.
“Given their experience and relentless development speed, Borja and his team are uniquely positioned to transform this space.”
The funding comes at a time when — as PYMNTS wrote recently — many enterprises hold lofty views about the potential for agentic AI, while also showing some hesitancy about using it.
“Despite growing capabilities, agentic AI is being deployed in experimental or limited pilot settings, with the majority of systems operating under human supervision,” that report said.
“But why are mid-market companies hesitating to unleash the full power of autonomous AI? The answer is both strategic and psychological. While the technological potential is enormous, the readiness of systems (and humans) is far less clear.”
For AI to function autonomously, PYMNTS added, companies need to trust not only the output, but the entire decision-making process that produced it. That sort of trust is difficult to earn and easy to lose.
Research by PYMNTS Intelligence shows that 80% of high-automation enterprises cite security and privacy as their top concern with agentic AI, while 62% had concerns about integration issues, and 57% were worried about the accuracy of AI-generated outputs.
“These figures reflect a general trend: agentic AI is being treated as a high-potential, high-risk technology that has yet to meet the compliance, reliability and trust thresholds required for enterprise-scale deployment,” PYMNTS wrote.
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