Flagship Advisory Partners | Payments and fintech consultancy

AI’s Impact on Payments & Fintech, Part 1: Introduction

Written by Joel Van Arsdale, Aigerim Smrckova & Taijasi Sharma, | Jan 17, 2025 2:45:19 PM

This is Part 1 of Flagship’s multi-part series assessing the potential impacts of AI on payments and fintech. Read Part 2 on Fraud Prevention here

Artificial Intelligence (AI) is all the buzz in fintech as we kick off 2025 and rightfully so. The inevitable impact of AI in financial services is massive and multifaceted. AI will change the way all of us experience financial services and reinvent performance and competitive dynamics within the industry. In this introduction to Flagship’s five-part series assessing the potential impacts of AI on payments and fintech, we introduce AI, outline the historical context for AI’s impact on our industry, and introduce the impacts of AI to be further explained in the subsequent parts of the series. 

What is AI? 

Artificial Intelligence (AI) is a technology that mimics human intelligence, enabling machines/software to execute functions previously depended on people and our ability to create, comprehend, and make decisions. AI can deliver human-like functions, such as answering questions by providing research and learning support. AI has the potential to exceed human intelligence (the so-called point of singularity). More practically, AI can already compute and scale better than humans, potentially releasing fintech businesses from a primary constraint. 

AI relies on massive amounts of data and computing power and is the culmination of decades of technological evolution. AI builds upon a series of innovations such as machine learning, neural networks, and natural language processing, all being powered by increasingly massive computing power. These advancements, some of which originated decades ago, allow machines to ingest natural/human data inputs, learn from these inputs, and output results in our desired format. There are different types of AI, including Generative AI, a form of “Narrow AI,” or AI designed for specific tasks only. GenAI is used today to generate text, sound, images, and video. Other forms of AI include Reactive Machines and Limited Memory AI, which include applications such as chatbots, autonomous driving, and playing Chess vs. a computer. AI is not yet fully aware and self-autonomous, but scientists expect these milestones in the future. 

Powering today’s rise of AI are numerous foundational technologies, as shown in Figure 2 below. As stated earlier, AI requires massive computing power, which is made possible via the ongoing evolution of chip technology. Massive amounts of data are another prerequisite for AI development, with most data coming from the Internet and social media/mobile applications powered by data infrastructure designed to record and store

AI and Its Precursors Are Not New To Fintech 

Artificial intelligence and robotics are not new to financial services. More so than most industries, financial services and especially payments, have relied on machines for decades to automate and accelerate transactions between people. Transaction authorization, credit underwriting, fraud detection, digital identities, and autonomous payments are all examples of machine-driven innovations that help us to transact on a daily basis more easily, securely, and effectively. While recognizing the decades-long technological journey to date, it is clear that AI is now pushing fintech beyond people-based dependencies. For example, fraud management models/machines are increasingly self-learning LLMs, not people-driven regression or rules-based models. We cover the impact of AI on fraud prevention in Part 2 of this series here [LINK].  

In Figure 3, we list examples where the usage of machine-/AI-driven decisions and robotics are well-established in the fintech industry today: 

  • Credit decisioning: credit scoring and predictive models such as those offered by FICO have been around for decades, always evolving and improving. Underwriting processes that once took days are now done in real-time. Beyond the traditional scoring algorithms that are generally regression-trained on application and credit bureau data, AI (machine-learning) based underwriting models use broader, more comprehensive data inputs to find incremental lending opportunities.
  • Transaction decisioning: all payments involve payer and payee decisions to accept or deny the transaction, with a card authorization being a prominent example. Once a person presents an electronic payment, machines make these decisions and have for decades. Processors (merchant and bank) and networks now deploy AI models to optimize conversion while balancing potential fraud.
  • Fraud prevention and identity verification: paramount to making good transaction decisions, machines today recognize and validate our digital identities to avoid bad actors. Payment fraud detection is one of the most obvious examples of AI today. In parallel, forms of digital identities and technologies used to validate our identities continue to arise and evolve.
  • Automated servicing: servicing of financial services has migrated towards machine-based automation for many years, starting with the advent of banking chatbots in the mid/late 2010s. Self-servicing, real-time notifications, chatbots, and personalized web or app user experiences are all technologies that allow servicing without needing a person on the other end.
  • Orchestration and smart routing: machines are now working to improve your checkout experience and to ensure your payment requests are optimized as they travel through the complex value chain of payments.

AI Disruptions Increasingly Visible  

2025 feels like a tipping point because we see and feel the buzz mounting, informed by both public and non-public observations. Publicly, there are a series of events that have us excited about AI’s tangible impacts on the fintech market as we start 2025. Some of these examples/milestones are summarized in Figure 4 below.

What’s Next 

We recognize that the impact of AI and its precursor technologies is already highly visible in fintech. Transacting in 2025 is light-years ahead of transacting in 1975 because of machines. However, we also recognize that real-time, digital payments are just the beginning, and that AI will have far-reaching impacts on the fintech industry over the next decade. The first impact, already well underway, will be ongoing improvements to fraud management. The second (potentially most financially consequential) will be a vast expansion of operations automation; the financial services industry is still riddled with slow, inefficient manual processes that can and will be automated. The third impact will be creating better customer experiences, leading to new revenue models. Each of these waves of impact will disrupt the landscape of providers of financial services, creating winners and losers. 

We will cover each of these impacts in subsequent insights published in the coming weeks, including: 

  1. AI’s Impact on Fraud Prevention in Payments & Fintech Read here
  2. AI’s Impact on Operating Efficiency in Payments & Fintech
  3. AI’s Impact on Product/UX and Revenue in Payments & Fintech
  4. AI’s Potential Disruptions to Payments & Fintech Competitive Landscape

Please do not hesitate to contact Joel Van Arsdale at Joel@FlagshipAP.com, Aigerim Smrckova at Aigerim@flagshipap.com, Taijasi Sharma at Taijasi@flagshipap.com with comments or questions.