Artificial Intelligence – more specifically, Generative AI – is the hottest topic in probably any company right now: around the world, businesses want to understand how they can tailor this technology and use it as a selling point to improve processes, products, and sales numbers.
But, to actually make the most of the possibilities offered by GenAI and its revolutionary potential, it’s fundamental to look beyond the buzzword and consider what it can improve in your company. In one of the most important events of the world of payments, Money 20/20 Europe, the perception was that there is already a clear understanding that AI cannot be just a trend for companies to try and leverage, but a strategically applied technology that, when used assertively, can have business-changing results.
What are those possibilities? How to look into GenAI as a way toward a goal, not as the end-goal itself? Let’s discuss the possibilities of Artificial Intelligence for payment companies and what industry leaders talked about the subject at Money 20/20 Europe:
One key aspect in which financial companies see extraordinary value for AI is fraud prevention: as fraudsters evolve and catching them before the fact becomes more difficult, Artificial Intelligence can help better understand the fraudsters' profile, behavior, and processes. There's a downside to this, though — on one hand, AI is being successfully used to hinder fraudsters, but on the other, it's also being used by the fraudsters themselves to improve their attempts.
As usually happens with anti-fraud processes, it's essential to stay ahead of the other side and constantly evolve risk analysis in order to be able to avoid fraud without causing too much friction for valid customers. Consequently, another major discussion going on around the whole event is centered on finding the balance between robust operations and smooth processes.
On one hand, businesses must follow compliance requirements, fit into the evolving regulatory landscape, and stay ahead of fraudsters; on the other, they have to deliver ever-better UX, create differentials to stand out from competitors, and be able to process more and more transactions faster and faster to scale successfully. So how to balance all of those needs without hindering the flow?
Compliance is not letting down anytime soon — in fact, with new high-risk verticals such as betting taking center stage and the rising need for cross-border payments, among other evolutions, compliance demands just tend to become stricter. It's important, then, to understand compliance and regulation not as obstacles, but as key pillars of the financial industry, helping protect and keep the standard for companies and customers.
With tokenization also being looked at by more and more companies, who turn to it as a way to better protect user and transaction data, AI comes along as a high hope here as well – but it also raises concerns regarding data protection and how data is used to train AI models. Thus, when considering possible applications of AI, looking inward to the company’s processes and how they can be improved with it can be an assertive strategy.
As we’ve said in the introduction, tech companies have already realized that having such a strong buzzword going around means a lot of actual potential developments and improvements, yes — but also that even if they don't see the value of investing in AI right now, their competitors and their customers are going to push them to.
With GenAI being such a huge trend, in time, customers will also become more mature in regard to it, and will be able to perceive which companies are applying it for true improvements, and which ones are just trying a catchier sales argument. Besides, as the months pass and AI tech quickly evolves, the businesses using it strategically will reap greater results, thus creating even more of a distance between them and competitors using AI — or saying they use AI — for buzz's sake through surface-level applications of the technology.
Thus, it's key to look at your customer's true expectations, consider their behavior and their requirements (we will look deeper into this later on this blog post), to then apply AI to improve on their overall experience, strengthen anti-risk processes, improve the customers’ experience, and think about new products and perspectives with Artificial Intelligence that benefit your business and your audience beyond just mentioning AI as a marketing argument.
As we’ve seen, the biggest challenge in the world of finance right now is not really applying Generative AI, but fully understanding it as a business strategy — it’s the so called “current shiny thing”, what everyone is looking at, but... Is it truly the answer to any and everything?
At least on the Money 20/20 stages, experts seem to understand that no, it is not. GenAI and Language Learning Models (LLMs) are certainly revolutionary, offering major potential for business improvements and new products, but a lot of the time, “traditional” AI such as machine learning might be the best tool. Or even no AI at all.
So how to decide between following the market trend or choosing the most suitable tech? The defining factor lies in another term: customer-centricity. By keeping customer needs and demands at the center of all business and product decisions, companies can define what truly matters, what should be the next steps, and what can/should be improved — and only then find the right technology to do that, not the other way around.
All in all, there are a lot of challenges surrounding GenAI and LLM, but the positive aspects may outweigh the risks. Thus, it is definitely high time for financial companies to dive into the tech and find the best ways to apply it, always considering which actual customer needs might be improved or solved with the support of Artificial Intelligence. Our next topic will focus on that–
Another hot topic for the usage of AI for financial companies — and beyond — is the creation of hyper-personalized processes and services, offering innovative and "tailor-made" experiences for the customers through new ways of interacting with a product. Considering the heavy regulatory requirements and internal processes financial companies deal with, AI can allow businesses to offer flexible UX without losing the strong level of control the sector demands.
Understanding and following the needs and the demands of the customers is what must drive all business strategies and choices, from when to use or not use GenAI to what new products to focus on. Here, it's important to also keep in mind at all times the core of the business, the company's values and what it strives for in the industry.
With those things at the center, better decisions can be made — and the industry definitely sees a difference in AI and other innovations applied to improve the core business, or to benefit other processes that surround it. Each company must evaluate which path makes more sense at each moment: improvements to the core business are much more easily perceived by the end-customer, but strengthening "backstage" processes can free up more time and resources which, in turn, can go to focusing on the core.
As we have shown, Artificial Intelligence opens up several opportunities for improvements and new solutions in the world of payments – and one of the major areas in which AI can be used is fraud prevention.
Here at PagSeguro, our Smart Fraud Prevention measures help cross-border merchants ensure security through machine learning algorithms trained with the more than 100 million cross-border transactions per year we process – check out our infographic and understand exactly how it works.
And to learn more about how PagSeguro can help you boost and strengthen your business in Latin America, click below to get in touch with our expert representatives: