The payments industry is increasingly defined by speed, security and precision, and generative artificial intelligence promises to transform every facet of financial services.
Yet, as Lisa McFarland, executive vice president and chief product officer at Ingo Payments, told PYMNTS for the series “What’s Next In Payments: Memo to the GenAI Companies,” if she were to sit down with Sam Altman or other AI leaders, her message would be clear: The payments industry needs more specialized solutions.
“There is a lot that we’ve had to develop internally,” McFarland said, emphasizing the gap between off-the-shelf AI tools and the bespoke needs of players in the financial services space. “You can take core tools, but you’ve got to do a lot of work from a development perspective and a learning and intelligence perspective internally.”
Against that backdrop, collaboration between generative AI companies and the payments industry may turn out to be essential to realize future opportunities. McFarland said Ingo itself engages with both AI developers and third-party service providers to enhance capabilities and address specific needs.
“We do reach out to a host of companies in areas where we are very focused on development and enhancement,” she said.
However, proactive engagement from AI companies is also important, particularly in designing solutions that align with the unique demands of financial services.
McFarland said customer service is a “low-hanging fruit” for AI, where tools like interactive voice response (IVR) chatbots and customer service representative (CSR) prompts are improving interactions and reducing costs.
“We’ve increasingly begun using AI-based tools in the technology area,” she said, adding that these tools, particularly in code analysis and completion, improve productivity for junior developers, enabling faster and more accurate delivery of solutions.
However, efficiency gains go beyond internal operations. Dynamic interactions powered by real-time analytics are reshaping customer experiences.
“We’re dynamically changing an experience with a customer based on behavioral or other data analytics,” McFarland said, emphasizing that these capabilities promise a future where AI-powered interactions are not just equivalent to human service but potentially superior, offering greater context, quicker resolutions and enhanced personalization.
In the high-security world of payments, risk and fraud management remain top priorities, as well as top opportunity areas for generative AI applications.
McFarland described Ingo’s use of AI to assess and monitor transactions, identify anomalies, and differentiate between legitimate and fraudulent interactions, saying of AI’s role in transaction location analysis, risk scoring and underwriting that Ingo is “increasingly identifying really unique ways to be able to identify good interactions.”
One of the most promising areas is AI’s ability to analyze behavioral patterns.
“There are ways that humans interact with applications… that are different than fraud actors,” McFarland said.
By identifying these nuances, AI systems can escalate responses dynamically, a capability that is “of intense interest” and critical to the payments ecosystem, she said.
While the benefits of AI are evident, McFarland said one challenge is security and data ownership. Tools designed to enhance efficiency, such as AI-based note-taking applications, often fall short of financial services’ stringent data protection requirements.
This issue is a barrier to broader AI adoption, highlighting the need for AI companies to develop solutions tailored to the specific regulatory and security requirements of the financial industry.
“In the payments and financial services space in general, you’ve got to be really careful about the tools you use, and the licenses and data protection associated with those licenses,” McFarland said, adding that AI solutions must prioritize robust data security frameworks, ensuring that financial institutions maintain ownership and control over sensitive information.
Instead of generic solutions, AI companies should collaborate with industry players to design models that address unique challenges like transaction anomalies, dynamic risk scoring and regulatory compliance, she said.
McFarland said she envisions a future where AI powers deeper personalization across customer engagement and service. She highlighted the potential of AI to create more intelligent, responsive interactions that not only address customer needs but anticipate them.
“The better those tools get… you could get to a place where they’re better than a direct human interaction,” she said.
This evolution is not limited to customer-facing applications. By adjusting interactions and responses in real time, AI can also streamline internal decision-making processes, ensuring that businesses respond swiftly and effectively to emerging challenges.
For AI companies, the message is clear: The payments sector is ready for collaboration, but it needs tools that prioritize security, specialization and scalability.
“The better those tools get… the better the outcomes for everyone involved,” McFarland said.
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