In this TNGlobal Q&A with Theo Spyrides, the Head of Product at Primer, we learn more about how artificial intelligence (AI) is a pivotal force that drives innovation in the rapidly evolving fintech ecosystem. With a background finance, payments, and product development, Spyrides leads the development of Primer’s enterprise-grade payment infrastructure, focusing on creating a seamless, open ecosystem that empowers businesses to take full control of their payment flows.
In this discussion, Spyrides shares his insights into the current impact of AI and the emerging trends poised to redefine the financial ecosystem in the coming years. From enhancing fraud detection and prevention to personalizing customer experiences, AI’s influence is multifaceted. He further explores how machine learning algorithms process vast amounts of data in real-time to identify and mitigate fraudulent activities, thereby improving security and customer trust. He also discusses the role of AI in streamlining cross-border transactions, making payments more interconnected and seamless across different markets.
Furthermore, the Q&A addresses the ethical and regulatory challenges that accompany the integration of AI into financial systems. Spyrides emphasizes the importance of transparency, fairness, and collaboration among stakeholders to navigate the complexities of AI adoption responsibly. Through this conversation, we gain a comprehensive understanding of AI’s transformative potential and the strategic approaches necessary for businesses to thrive in an AI-driven fintech landscape.
How is AI currently shaping the payments and fintech industries, and what key trends do you see emerging in the next few years?
Across payments and fintech there are a few key areas where we are seeing the greatest impact and potential for AI. The most obvious is fraud detection and prevention. In the past, fraud checks in the payment journey would require manual handling through a team of analysis and would typically take place post-authorization.
Now, merchants are able to apply machine learning and AI to process vast amounts of data in real time to detect abnormal transactions. That means that automated checks can take place pre-authorization, eliminating potential delays and creating a much better customer experience.
Not only that, but AI systems are becoming increasingly adaptive, so every time there’s a change in behavior from fraudsters, these systems are learning how to recognize them and can stay ahead of evolving threats. They can also work across more of the customer journey, aggregating data sets from different sources. This level of sophistication creates huge efficiencies and ensures detection measures are essentially ‘invisible’ to the end user, while also reducing operational costs for businesses.
Another area we’re seeing a big impact on is the checkout experience, where AI can increasingly offer personalized recommendations. This is not new: large players like Amazon have been able to analyze customer behavior and preferences to provide tailored product suggestions for some time. But with AI, we are going to see more merchants able to access this technology, increasing customer engagement and revenue and ‘leveling the playing field’ for more merchants when it comes to the customer experience.
An interesting trend that we are seeing is personalization of the payment method itself, whereby customers are presented with their favorite payment method, rather than a list of options. We are even starting to see this extending to a ‘financial companion’, a chatbot that’s almost like a financial advisor that will recommend purchases based on deals and personal preferences for individual customers.
Finally, AI is creating more competition for payment providers, which must differentiate themselves by improving the customer experience. We’re seeing this in the BNPL space, with tailored BNPL plans designed with the end user in mind.
Whether it is being able to support safer, speedier payments or vying for a share of the wallet among the competitive payments space, AI will definitely have a greater presence across the payments journey in time to come.
Payments have traditionally been a fragmented space. How can AI contribute to making payments more interconnected and seamless across different markets?
A common misconception is that AI in payments is a silver bullet that will solve all of payments’ challenges. This is not the case, and cross-border fragmentation is one example. The fragmentation in the payments space is mostly due to different compliance requirements and payment methods across markets, and the complexity of bringing this together.
AI is not going to solve this, but what AI will do is enhance existing technologies, like unified payments infrastructures, to solve some pain points brought out by this fragmentation. For example, showing the right payment method at the right time for the right market, smart routing to seek out the best providers for optimized payments or applying 3DS to the right payments can be automated and enhanced by AI, ultimately contributing to more seamless cross-border payments.
In what ways can AI-driven innovations in payments contribute to enhancing financial inclusion, particularly in regions like APAC?
In diverse regions like APAC, AI-driven innovations can support merchants to unlock complete localization across websites and checkouts. This can incorporate language, and preferred payment methods and is becoming increasingly sophisticated in terms of the user journey and buying preferences unique to each market. That’s incredibly powerful, enabling native experiences for a wider range of consumers.
In our region, there is also a significant unbanked population. AI can then be a tool to customize risk models to offer particular payment options for these groups of consumers who may typically struggle to get any kind of lending or credit product. Finally, AI can also help to automate or speed up real-time loan requests or credit approvals, making these processes far more accessible for consumers and businesses around APAC.
Fraud remains a major concern in payments. How do you see AI evolving to address this challenge?
Fraud is a major concern in payments, and this will likely continue. The challenge with generative AI is that although protections are getting more sophisticated, so are fraudsters. It is now easier for AI to impersonate a person, or to identify and penetrate potential weak spots along payment systems. As a result, fraud efforts must continue to evolve and be on the cutting edge, or risk being susceptible to bad actors. Eventually, we will see a scenario of AI vs AI when it comes to the fight against fraud.
Currently, there are many companies such as Riskified and Sift that are using AI to analyze vast amounts of data to identify anomalies and suspicious activities in real time. Fraud prevention technology will likely evolve towards active prevention, where it can learn to predict fraud ahead of time. This is why community-based prevention is no longer a nice to have, it is a necessity. The larger the data set, the more information you have to train and build these AI models to enhance fraud-prediction capabilities.
As AI becomes more integrated into payments, what ethical and regulatory challenges do you foresee, and how can the industry address them?
Data collection would be one ethical consideration that the industry will have to grapple with. Each day, more and more data is collected and shared. How can we ensure the right regulatory frameworks to protect the consumer while not hindering progress for business – particularly when you consider working across regions?
Another consideration is around biases and fairness in the data collected and how it is applied. We are beginning to see AI players such as Perplexity exposing the rationale in their responses. This transparency allows for that layer of human discernment, which will be critical as we are using these tools more and more.
What potential does AI have to revolutionize or future-proof cross-border e-commerce and travel payments in the coming decade?
Again, when you consider localization, AI and machine learning can be applied to seek out the best payments for consumers in cross-border e-commerce and travel payments. Taking this a step further, we’re seeing fintechs developing solutions to help travel companies design individualized payment plans for travelers. This degree of customization can be a game changer in unlocking more payments potential and capturing more revenue.
One of the pain points of cross-border transactions where AI could be applied is in FX, with dynamic currency prediction tools. Leveraging real-time predictions for greater accuracy in the fluctuating currency and dynamically adjusting prices has huge potential.
Finally, AI-enabled smart routing can optimize payments for travelers by tapping into local card networks which can operate on more cost-effective rails, saving on costs. This technology is happening already – for example, through our platform Primer, merchants can apply smart routing to optimize on costs, or conversion rates. What AI will do is make that decision automatically, which again will benefit those smaller players that historically would not have the team of analysts that can design that.
The key benefit of AI in future-proofing is in the advent of systems that are designed with the ability for continuous, reinforcement learning. This is really powerful, as I mentioned before in areas like fraud prevention.
Collaboration is increasingly emphasized in the fintech ecosystem. How can AI facilitate greater collaboration among payments, fraud prevention, and other operational teams?
As an open infrastructure, Primer is at the forefront of the evolution of the payments industry towards greater collaboration. The traditional siloed PSP model is no longer feasible and we’re seeing a shift towards a more open flexible payments ecosystem. AI is not driving this, but it can be a really powerful tool to create better customer and merchant experiences, create greater efficiencies, and lower the barriers to insights and execution of strategies.
Looking forward, what is your vision for how AI will shape the global payments landscape in the next decade? Do you see any new use cases emerging for GenAI?
AI will continue to shape the global payments landscape by increasingly taking on the role of an analyst, helping merchants and businesses make important decisions quickly to unlock more payment potential. We’ll see the management of payments become more streamlined, hyper-customization enhanced, and highly sophisticated payment systems with fewer resources needed – ultimately delivering much higher ROI for payments. Gen-AI will continue to improve its logic, and we will see payments managers able to shift focus to more strategic business decisions rather than execution.