The payments and banking sectors are undergoing significant changes as artificial intelligence (AI) becomes an integral part of operations. With tools like generative AI (GenAI) finding applications across services, companies are navigating challenges to ensure they optimize its benefits while addressing concerns about costs, governance, and operational utility. As the industry adapts, there is increasing emphasis on how AI can enhance efficiency, address fraud, and improve customer experiences in a highly regulated environment.
When AI’s integration into payments innovations is revisited, earlier discussions primarily revolved around its high-level potential without delving into the specifics of implementation. A few years ago, the focus was on AI-driven chatbots and predictive analytics, whereas now, the emphasis has shifted toward specialized applications, such as fraud detection and dynamic customer support. Current trends also demonstrate a deeper understanding of AI’s limitations, including data privacy and operational costs, reflecting a maturing perspective compared to initial speculative concepts.
What Are the Practical Applications Driving AI in Payments?
Companies in the banking and payments space are actively exploring practical uses for AI beyond theoretical capabilities. According to Mark Sundt, CTO at Stax Payments, operationalizing AI requires careful governance to prevent mishandling of sensitive data and mitigate associated risks. Sundt emphasized that many services now embed AI tools, making governance a critical focus to ensure technologies do not inadvertently compromise data security.
The use of smaller, agentic AI models rather than large, generalized language models (LLMs) is gaining traction. These specialized models are tailored to address specific challenges, offering more cost-effective solutions without sacrificing functionality. Sundt illustrated this shift using customer service as an example, where focused AI models are designed to resolve specific issues, such as optimizing payment terminal configurations for better interchange rates.
How Is AI Assisting in Fraud Detection and Risk Management?
AI is proving to be a valuable asset in fraud detection and risk management within the payments sector. Sundt highlighted patterns like large transactions followed by reversals or newly created accounts as key indicators of fraudulent behavior. By analyzing temporal attributes and transactional behavior, AI enhances decision-making processes, helping organizations identify and mitigate fraud effectively.
Looking forward, Sundt believes that reasoning capabilities represent the next challenge for AI development. Current AI models excel at summarizing and categorizing data but often struggle with interpreting incomplete information. He suggested that enabling AI to mimic human-like reasoning in ambiguous scenarios could significantly advance areas such as fraud prevention and customer engagement.
Sundt also acknowledged the gap between AI providers’ rapid technological advancements and the slower, compliance-focused pace of the financial industry. “The financial sector doesn’t move at the speed of tech. It’s crucial for AI companies to understand our industry’s unique challenges and design solutions accordingly,” he noted.
This shift toward more agile, customized AI models mirrors broader technological advancements, such as the transition from centralized computing to distributed cloud systems. The banking and payments industries are expected to continue refining their AI strategies to balance innovation with the regulatory requirements that define the sector.
For readers interested in the progress of AI within payments, it is essential to monitor how companies address governance, operational costs, and the evolving needs of customers. Specialized, smaller-scale AI tools may play a pivotal role in shaping the future of the industry by tackling specific challenges without incurring excessive costs. AI’s success in payments likely depends on its ability to adapt to complex, real-world scenarios while maintaining compliance with industry standards.
