PayNearMe’s Vision for the Future of AI in Payments
For the last 17 years, we’ve focused on understanding our clients’ payment problems and applying the best technology available to solve them. That’s what led us to develop the PayXMTM platform and Payment Experience Management to accelerate payments and reduce the total cost of payment acceptance.
We’re taking the same approach to AI. When our clients told us they wanted to reduce manual work and operate more efficiently, we began investing in agentic AI to solve real payment problems. This includes our decision to acquire the technology assets and hire key employees of Marr Labs, a company that built enterprise AI technology for voice, messaging and workflows in complex, regulated industries.
Payments come with a high bar for reliability, security and compliance. That’s why we believe the future of AI in payments isn’t just about building smarter agents. It’s about connecting those agents to the right systems, putting appropriate guardrails around them and determining where AI can, and can’t, make a difference.
That mindset guides our approach to AI at PayNearMe and explains why we believe the most interesting chapter of AI in payments is just beginning.
Where AI can improve payment experiences
We’ve heard from clients who are being told they need AI but don’t want to adopt it simply to check a technology box. The real question is where AI can meaningfully improve business productivity and customer experiences.
For businesses, the biggest opportunity we see right now is reducing manual work across the payment journey. Many payment and servicing workflows involve repetitive tasks that follow defined rules and procedures. For customers, AI can make basic self-service tasks faster and easier. Together, AI offers an opportunity to automate some of the routine work and handle basic tasks more quickly and consistently.
However this is only the beginning of how AI can advance Payment Experience Management.
We believe one of the next important stages of AI in payments will be agentic systems that can help customers complete tasks in real time rather than simply perform isolated automated functions.
Agentic AI supercharges Payment Experience Management
Payment Experience Management helps businesses optimize the end-to-end payment journey for customers and the support and operations teams behind each payment. By improving all three experiences, businesses can accelerate payments and reduce the total cost of acceptance. Agentic AI can advance that goal by making certain payment-related support and operational tasks more proactive and autonomous.
Our Payment Experience Gap report shows why support is a valuable place to start. In its model of a typical mid-size lender, support is the largest of three cost categories, averaging approximately $2.70 per payment. Helping customers resolve payment issues through self-service could reduce some of that effort and make support teams more available for complex situations.
Agentic AI can go beyond generating responses by taking action across defined workflows. For example, an AI agent would understand why a payment failed and then look at available payment options that haven’t failed in the past. The AI agent would then offer an alternative payment method and proceed to take the payment.
In other words, instead of simply explaining why a payment failed, an AI agent will interpret context, access real-time data and standard operating procedures, apply business rules and execute the next best action. All while keeping the customer in the self-service channels.
But the customer-facing experience is only one part of Payment Experience Management. We’re beginning with customer self-service while exploring how agentic AI could also help the support and operations teams that manage exceptions, recovery and reconciliation.
Why embedding in the payment platform matters
For an AI agent to resolve a payment problem, it needs more than intelligence. It needs authorized access to the relevant transaction data, payment methods, business rules and servicing workflows. Without that access, an AI agent may be able to explain a problem without having the tools required to resolve it. That distinction matters.
The goal isn’t simply for an agent to produce a useful response. It’s to give the agent the context and capabilities required to take appropriate action.
Our approach is focused on connecting AI agents with the payment capabilities and workflows required to help resolve real payment problems. That’s why PayNearMe embeds agentic AI directly into PayXM rather than treating it as a separate layer.
Why PayNearMe acquired Marr Labs
PayNearMe had already begun developing agentic AI capabilities in response to what we were hearing from clients. As that work progressed, we concluded that agentic AI needed to be closely connected to our payment technology and that adding Marr Labs’ specialized expertise could accelerate those efforts.
Effective agentic AI requires both sophisticated AI technology and a strong understanding of the workflows the AI is being asked to support. PayNearMe brings years of experience building payment infrastructure, transaction capabilities and workflows. Marr Labs brings specialized experience building and deploying AI agents for regulated industries.
That expertise complements PayNearMe’s existing product, engineering and AI teams as we apply agentic AI to complex payment challenges.
For more perspective on the thinking behind the combination, check out the conversation between PayNearMe Founder and CEO Danny Shader and Marr Labs Co-Founder and CEO Dave Grannan.
The goal isn’t more AI. It’s better payment outcomes.
Our goal is the same one we’ve had for the last 17 years: understand the problems our clients and their customers face, then apply the right people and technology to solve them.
We believe AI can make payment servicing and collections more efficient, give customers better self-service experiences and help resolve more interactions from start to finish.
But getting there requires more than a capable AI model. It requires people for the moments that demand empathy and judgment, guardrails that help AI operate responsibly and payment technology that gives AI the context and tools it needs to take appropriate action.
We’re not interested in AI for AI’s sake. We’re interested in what can happen when specialized AI expertise and payments expertise come together to make payment experiences work better for clients and their customers.
