Generative AI Reshaping the Future of Payments: Opportunities and Challenges

Memo to GenAI Companies: The Future of Payments Needs Your Focus | PYMNTS.com

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.

GenAI Is Cleared for Takeoff in Travel Payments | PYMNTS.com

The future of payments isn't just about what's possible -- it's about how effectively those possibilities are realized. When it comes to advancements like generative artificial intelligence (AI), where the opportunities are vast, balancing innovation with stability becomes crucial. That's particularly the case for sectors as critical as payments. "There's so much blurring of the lines between the exciting stuff we see in the ChatGPTs of the world and the real-world application of machine learning. We have to keep it pragmatic and focused to ensure we're doing cool new things without jeopardizing stability or reliability," Tom Randklev, global head of product at CellPoint Digital, said during a discussion for the PYMNTS series "What's Next In Payments: Memo to the GenAI Companies." This underscores a key challenge for tech providers: aligning rapid innovation cycles with the long-term stability and compliance requirements of banking and payments. Against this innovation backdrop, Randklev advocated for greater collaboration and a "frenemy" approach to innovation, where competitive entities work together to enhance the broader ecosystem. As the payments industry explores AI applications, certain use cases have emerged as natural starting points, offering immediate value while minimizing risk. Among these early applications, fraud detection has proven particularly promising, combining the power of AI with the critical need for enhanced security. Fraud detection stands out as one of the more promising applications of GenAI in payments, especially today. "Generative AI fits best and fits easiest in this area [of fraud]," Randklev said, referencing CellPoint Digital's own strides in predictive and adaptive modeling. The ability to leverage real-time behavioral analytics allows fraud systems to stay ahead of emerging threats, including deepfake fraud and other sophisticated cyber tactics, he said. "Our experience has been that we're able to do more predictive and adaptive modeling," Randklev said. "This gives us a roadmap opportunity to look at more collaboration via confederated learning for secure exchanges of fraud insights. With zero latency fraud detection, we're building an ecosystem that is not only reactive but proactive." The integration of advanced authentication modules, such as biometrics, further enhances security. These tools provide layered defenses while maintaining seamless user experiences -- a critical balance as fraudsters evolve their techniques. GenAI's utility extends beyond fraud prevention into payments orchestration -- the intelligent routing and management of payment transactions -- and compliance, especially within the highly regulated travel industry. CellPoint Digital's expertise in this sector has allowed it to capitalize on GenAI for refund processing and fraud optimization. "There have been a lot of regulations, especially within the U.S. for the travel sector," Randklev said. "Leveraging our deep expertise in airlines, combined with GenAI, allows us to address these challenges efficiently and effectively." By automating and optimizing workflows, CellPoint Digital ensures that airlines and travel operators can comply with evolving regulations while improving operational efficiency. This adaptability showcases how AI-driven innovation can align with stringent compliance requirements without sacrificing performance. Looking ahead, Randklev sees tremendous potential for AI to reshape the travel industry. The emergence of the Offer Order Settle Deliver (OOSD) model -- which connects customer offers, orders, payments and delivery within the travel industry -- exemplifies how technology can streamline operations and enhance customer experiences in traditionally slow-moving sectors. "We're seeing a transformation in the travel industry," Randklev said. "As customers explore OOSD, it creates a perfect storm of new and exciting use cases for AI and ML. The intersection of these technologies with travel retailing is set to redefine how airlines and other operators engage with their customers." Personalization is another area where GenAI is driving significant improvements. "Through payment orchestration, we're creating hyper-personalized, dynamic forms of payment presentation," Randklev said. "We're meeting our customers where they want to do business, when they want to do business, and providing the ability to pay however they want to." This approach enhances customer engagement and improves conversion rates for merchants. By supporting alternative payment methods and catering to unbanked populations, companies like CellPoint Digital are fostering inclusivity while optimizing the customer experience. "This creates better conversion for merchants because they're offering not just the standard payments we think of in the U.S., but also alternative methods that customers increasingly depend on," Randklev said.

Payment Processing Leads Charge in Modernizing B2B Financial Ecosystems | PYMNTS.com

Game-changing innovations like artificial intelligence (AI) don't discriminate. They reshape industries across the board. "Generative AI has the potential to change multiple industries, and the payment industry is no exception," Boost Payment Solutions Chief Technology Officer Rinku Sharma told PYMNTS during a discussion for the series "What's Next in Payments: Memo to the GenAI Companies." AI offers a transformative opportunity to streamline, optimize and elevate operations, particularly B2B payments that have traditionally been steeped in intricate workflows and tend to be reliant on manual, legacy processes. "It's about balancing innovation with stability and compliance to ensure we're delivering value while protecting our ecosystem," Sharma said of Boost's own approach to leveraging AI. "Public AI systems, like chatbots, can't simply be lifted and shifted for sensitive applications. Private AI offers greater control over data access and compliance," he said, underscoring the importance of private AI models for enterprise use cases. From automating invoice reconciliation and fraud detection to enhancing real-time decision-making in transactions, AI has the potential to unlock new payments efficiencies that were previously inaccessible through legacy methods. But as the payments landscape shifts, precision and responsibility in AI implementation should be paramount. At Boost, the focus is on leveraging generative artificial intelligence (GenAI) to tackle key challenges and enhance efficiency across payments workflows and data. "We are actively adopting it in our day-to-day activities," Sharma said, noting that the company is exploring various applications, including finding ways to further enhance their proprietary technology's fraud prevention, automation and dynamic pricing. Fraud detection is a top priority, Sharma told PYMNTS. "GenAI can analyze transaction patterns in real-time and identify anomalies, flagging potential fraudulent activities more accurately than traditional methods," he said. "This enhances security, reduces financial losses and builds customer trust." Automation is another area ripe for innovation. Boost has long prioritized efficiency through its core straight-through processing technology, and now the company is delving into how AI can take these automated solutions to the next level, unlocking even greater potential. "Our buyers and suppliers process hundreds of payments daily, often in various formats," Sharma said. "GenAI will dramatically reduce the time to market and operational load by adapting quickly to new platforms and formats." Boost is also enhancing its dynamic pricing optimization tool, Dynamic Boost, through AI. "With GenAI, we can adjust prices in real time, maximizing revenue while considering market conditions, competitive pricing and customer demand," Sharma said. Personalization has long been a challenge in the complex B2B payments ecosystem, where workflows and business relationships vary widely. According to Sharma, GenAI is poised to bridge these gaps. "At Boost, personalization is at the forefront of what we do," he said. The company uses natural language processing (NLP) and optical character recognition (OCR) to extract relevant data from payment notifications, match them with purchase orders and automate payment processing. "This ensures timely payments, reduces manual effort and strengthens supplier relationships," Sharma said. On the accounts receivable (AR) side, Boost is exploring predictive analytics. "By analyzing historical payment data, GenAI can forecast which customers are likely to delay payments," Sharma said. "This allows businesses to proactively engage with customers, offering personalized payment plans or reminders." AI advancements bring both opportunities and risks. Sharma emphasized the need for responsible adoption: "We are adopting a Responsible AI framework, focusing on robust strategies and control mechanisms to address cybersecurity threats." Education and collaboration are key. Boost works closely with clients to ensure their systems are resilient to cyber threats and compliant with evolving regulations. As fraudsters become increasingly sophisticated, GenAI is a critical tool in the fight against financial crime. Sharma stressed the importance of staying ahead: "The same tools we have today, bad actors also have. It's about staying several steps ahead and detecting fraud in real time." Boost integrates GenAI with predictive analytics to monitor transactions for fraud and recommend preventative actions. Looking ahead, Sharma sees potential in combining GenAI with blockchain. "An immutable payment transaction system powered by blockchain and GenAI could significantly reduce fraud opportunities," he said. "GenAI has tremendous potential, but we must tread cautiously," Sharma added. By integrating AI responsibly and innovating across fraud prevention, personalization and industry-specific solutions, Boost Payment Solutions is setting a new standard for the industry.