Dennis Kettler is Global Head of Data Strategy and Data Sciences at Worldpay.
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If you’ve been paying attention to the financial services industry, you know one thing for sure: AI is no longer a futuristic concept—it's here, and it's changing everything. But while the idea of AI revolutionizing payments sounds exciting, the journey hasn’t exactly been smooth.
AI adoption has skyrocketed over the past few years, particularly after the pandemic forced financial institutions to rethink how they operate. The numbers don’t lie. The global market for AI in financial services is projected to grow by $16.2 billion within 5 years. Banks, insurers, and payment processors are all diving headfirst into the AI pool, eager to streamline processes, enhance fraud detection, and create hyper-personalized customer experiences.
But here’s the catch: for all its potential, AI integration isn’t without its share of headaches. Many businesses have realized that their data—the very foundation of AI—is often locked away in outdated systems, fragmented across departments, or just plain messy. And even when the data’s in decent shape, there’s the tricky matter of ensuring compliance with a maze of ever-evolving regulations.
Add to that the fact that cybercriminals are getting smarter, and suddenly, building a robust AI-driven payment system feels like trying to assemble a high-tech puzzle while the pieces are constantly shifting. Yet, despite all the obstacles, companies are pushing forward.
In the last year alone, giants like JPMorgan Chase reported productivity boosts of up to 20% thanks to AI coding assistants, while NatWest teamed up with OpenAI to strengthen fraud prevention, a critical move considering the UK lost £570 million to payment fraud in early 2024. And it's not just the big players. Smaller financial institutions are also leveraging AI to boost efficiency, save costs, and deliver better customer experiences.
Automation is doing more of the heavy lifting, freeing up human experts to act more like strategic advisors than back-office processors. The question is: how can companies harness AI’s power without drowning in data issues, outdated systems, or regulatory red tape?
That’s exactly what we wanted to figure out. So, we reached out to an expert who’s been deep in the trenches of AI-driven payment solutions for over a decade. From optimizing billing and settlement processes to enhancing fraud detection systems, Dennis Kettler's experience spans the entire payments ecosystem. And let’s just say, his insights are eye-opening.
In the conversation that follows, you’ll hear firsthand about the biggest challenges and opportunities facing businesses.
R: Can you share a bit about your career journey and how you developed your expertise in fintech and payment solutions?
D: After completing my undergraduate and graduate studies in mathematics, I transitioned into the field of data analysis and predictive analytics. My initial focus was on predictive insights, and automation.
Approximately 13 years ago, I entered the financial services sector, bringing extensive experience and discipline in data and artificial intelligence. I began applying this expertise in areas such as billing, settlement, payment optimization, and client experience.
Although I did not have a background in payments at that time, I utilized my previous experience in retail and credit issuing, combined with my proficiency in algorithms and AI, to effectively drive value for Worldpay.
R: What are some of the most significant changes you’ve witnessed in the payments industry over the years, particularly with the rise of AI?
D: The three significant changes that immediately come to mind are proliferation, acceleration, and sophistication. While artificial intelligence is not a new concept, its proliferation has markedly increased.
Previously, AI development was confined to specific teams with specialized expertise. Today, AI is accessible to a broader range of individuals and teams, resulting in an acceleration in its application and a decrease in time to market. Additionally, the sophistication of AI has advanced significantly. Tasks that were unfeasible a decade ago, or even five years ago, are now achievable due to advancements in AI and cloud infrastructure.
R: Integrating AI into financial services comes with both opportunities and challenges. From your experience, what are the biggest obstacles companies face when adopting AI-driven payment solutions?
D: In my experience, the three largest obstacles in integrating and adopting AI-driven payment solutions are:
- A foundational challenge is the handling of data. Many overlook the critical importance of data in leveraging AI. Financial services often deal with vast amounts of data stored in siloed environments, which come in various formats, and with inconsistent definitions. Managing the quality of this data, proper understanding of the data, and effective integration is a significant challenge.
- From an AI development perspective, a large challenge is integrating AI into existing legacy systems. This requires not only technical adjustments but also a cultural shift within organizations to embrace new technologies.
- The final challenge involves navigating the global regulatory landscape and ensuring data privacy. As companies utilize data, they must ensure robust privacy controls, model risk management, and model transparency to comply with regulations and build trust with stakeholders.
R: Fraud detection has been one of the key areas where AI has had a major impact. What advancements have you seen in fraud prevention, and what challenges still need to be addressed?
D: Fraud solutions have been one of the more visible benefactors of AI advancement. One of the biggest improvements driving fraud detection has been in entity resolution and the ability to more clearly connect devices, accounts, transactions, and other disparate sources of information to create a more accurate and comprehensive view of relationships and associated activity.
Additionally, there has been a substantial increase in the ability to adapt to fraudulent trends in real-time. AI enables rapid adjustment to emerging trends, allowing for timely intervention in potential fraud activity.
Lastly, AI has significantly enhanced the accuracy of fraud detection systems by reducing friction and minimizing both false positives and false negatives. This improvement is crucial as it ensures legitimate transactions are processed smoothly while effectively identifying fraudulent ones.
Many of the challenges within Fraud detection are similar to those of broader AI adoption. For instance, despite advancements, challenges remain in ensuring high-quality data and seamless integration across various systems and platforms. Poor data quality can lead to inaccurate fraud detection results.
Lastly, while AI is improving the performance of fraud detection systems, it is simultaneously increasing the sophistication of bad actors.
R: AI-powered payment technologies are evolving rapidly. How do you see the role of financial professionals changing as AI continues to automate and streamline payment processes?
D: While AI is improving our ability to optimize payment processing, it is also changing the role of the payment professional. For instance, AI is increasingly allowing for automation of operational tasks, enabling us to focus more on the interpretation of data and AI insights and their strategic application.
Specifically, this automation allows us to act more broadly as translators for our clients and stakeholders. AI allows us to play a more consultative role thereby improving client experience. As a merchant acquirer, for example, we leverage AI to improve all aspects of the payments lifecycle. However, it also allows us to act as a more focused and purposeful strategic advisor.
R: Data privacy and ethical concerns are at the forefront of AI adoption in banking and payments. How do you approach balancing innovation with responsible AI implementation?
D: I do not fundamentally believe that a balance is required between focusing on innovation and being responsible in AI implementation.
These ideas are not mutually exclusive nor does one have to negatively impact the other. In fact, I strongly believe that proper governance including policy, controls, and oversight indeed acts as an accelerator for innovation. In my experience, clear policy, guidelines, and process allow developers to freely explore and innovate safely with confidence.
Lack of clarity or ill-defined governance frameworks lead to developer uncertainty, slows development, and stifles innovation.
R: Looking ahead, what are the most exciting trends in AI and payments that you believe will shape the future of the industry in the next five to ten years?
D: As noted previously, AI will continue to improve the efficacy of payment systems and relevant decision points: fraud detection, authorization rate improvement, sophisticated customer due diligence (CDD) and know your customer (KYC), etc.
It will also continue to shape the role payment professionals play when helping merchants and retailers define their payments strategies. For example, the use of AI can allow for greater personalization and payment outcomes while also providing unique insights that can all lead to a greatly improved customer experience.
Additionally, I expect to see improvement and acceleration in embedded finance both in terms of seamless integration as well as in core capabilities like lending. Lastly, given regulatory pressures and improvements in AI, I expect to see significant gains in transparency.
