What’s Next In Payments: An Increasingly Thin Regulatory Tightrope Will Be BaaS’s Proving Ground

Faster payments are transforming the financial landscape, but with speed comes heightened fraud concerns. Real-time payments move instantly, making them attractive to both consumers and fraudsters. However, the reality is more complex — real-time payment systems can enhance fraud prevention when combined with advanced security measures. According to experts with the BAI, fraud rates on real-time rails such as the RTP® network and the FedNow® Service are substantially lower than those for traditional payments such as automated clearing house (ACH), wire transfers and checks. Financial institutions (FIs) are leveraging artificial intelligence (AI)-driven monitoring, real-time transaction analysis and tokenized processing to detect and prevent fraudulent activity before it occurs. In fact, 94% of organizations now use AI and machine learning to identify fraud risks based on transaction behaviors, helping to reduce losses while maintaining the speed of instant payments. Consumers demand better fraud protection from their banks, with 75% stating they would switch FIs if they found security measures inadequate. Additionally, 57% of consumers say improved fraud detection is one of the top priorities they want their banks to address. These trends signal a shift in expectations: Speed alone is no longer enough; security is just as critical. The good news? Real-time payments prove that instant transactions and robust security are not mutually exclusive. By implementing AI, real-time fraud detection and stronger authentication, FIs can ensure that faster payments remain safe for businesses and consumers.

Despite rapid advancements in digital payments, healthcare transactions remain frustratingly slow and outdated. Patients accustomed to seamless digital transactions elsewhere are still dealing with lengthy reimbursement wait times and cumbersome billing processes. The culprit? Many healthcare providers still rely on legacy payment methods — paper checks, manual claims processing and outdated systems that create inefficiencies at every step. This reliance on traditional payment rails isn’t just inconvenient — it’s costly. Research shows that 48% of healthcare providers still process claims manually, a factor contributing to rising payment errors and delays. Claims inaccuracies increased from 43% in 2022 to 55% in 2024, fueling a surge in denied claims and refund requests. Meanwhile, the rising complexity of healthcare billing is putting even more pressure on outdated systems. At the same time, patient expectations are evolving. While just 39% of patients preferred instant healthcare disbursements in 2023, that number surged to 77% in 2024. Consumers now expect the same efficiency in healthcare payments that they experience when shopping online or transferring money digitally. Providers that fail to modernize risk declining patient satisfaction, delayed revenue cycles and operational inefficiencies. The good news? Change is happening. Sixty-six percent of small to mid-sized businesses (SMBs) in the healthcare provider sector used real-time payments in the past year, and third-party platforms are accelerating the transition to digital disbursements. These innovations aren’t just streamlining workflows — they’re improving cash flow, reducing administrative burdens and enhancing patient trust. In a time of rising healthcare costs and increasingly complex billing models, modernizing payment processes isn’t just an efficiency upgrade — it’s a strategic necessity.

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. The Front Lines of AI Applications Within Payments 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. The Next Chapter of AI in Payments 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.