The digitization of everyday life is transforming nearly every consumer touchpoint, particularly payments.
As the world continues to hit new levels of daily digital engagement, generative artificial intelligence (AI) is emerging as a crucial optimization driver within future-fit solutions like embedded finance.
That’s because AI capabilities promise to redefine and better protect financial engagements through, among other things, next-generation personalization on the end-user side and identity verification and compliance automation on the provider side.
Given the exponential growth of eCommerce, digital wallets and embedded payments, today’s firms need not only protect themselves against a rising tide of modern fraud but also find new ways to acquire and retain customers within a landscape that is constantly evolving, where both incumbents and innovators exist as competitive threats.
Still, as exciting as AI’s commercialization across the marketplace has been, the state of hype surrounding the technology will likely falter if no long-term, practical applications that demonstrate AI’s ongoing utility across industries emerge.
Fortunately, generative AI is bursting onto the scene at a critical moment during the intersection of today’s offline-to-online journey.
By integrating AI technologies into their solutions, firms focusing on building future-fit digital engagement channels, particularly within payments and finance, can harness winning efficiencies that drive differentiation and accelerate sustainable growth.
Read also: Generative vs Predictive AI’s Role Across the Future of Payments
PYMNTS research in “How The World Does Digital” showed 29% year-over-year growth in the use of Big Tech’s digital wallets like Apple Pay, Google Pay and Samsung Pay.
Given how many day-to-day consumer touchpoints increasingly rely on significant software components, generative AI will impact, at least in some manner, how businesses engage with their customers, and how they compete.
Historical frictions along the payment journey and within traditional financial service offerings have created an attractive white space opportunity where generative AI can be harnessed to enhance value.
“Consumer convenience plays to the very front of this one, where embedded payments have already made the experience incredibly smooth,” Tom Randklev, global head of product at payment orchestration platform Cellpoint Digital, told PYMNTS last week. “It’s a one- or two-click experience to get from product selection to a payment, and I think generative AI will continue to accelerate that.”
Because the latest generation of AI can undertake non-closed loop, iterative tasks in real time, it offers an upgrade from earlier generations of predictive AI.
The technology’s capabilities are already enacting sweeping changes across the ways in which end-users interact with, and what they expect from, the digital tools that make up much of modern life by streamlining previously complex processes in highly systematic and scalable ways.
That’s why industry observers are increasingly keen on generative AI’s potential to automate and optimize everything from identity verification to nontraditional credit scoring, two areas where AI applications can score an immediate, easy-win impact.
AI capabilities shine when applied to complex processes with large data volumes, particularly in marketplaces where speed to discovery can give a firm an edge.
Amias Gerety, partner at QED Investors, told PYMNTS last month that he believes, at least right now, that the most interesting areas where AI is being applied lie in fraud, risk management and underwriting because those are industries where practitioners are sophisticated and can use AI as a tool to get fast answers while ensuring that results are accurate.
But generative AI can also boost revenues through increased personalization of services.
“AI is your best bet — particularly as transactions increasingly become digital,” Mike Foster, president and CEO of SymphonyAI NetReveal, told PYMNTS last month.
A part of the potential of generative AI versus its earlier cousins including predictive AI and machine learning (ML) automation is that generative AI can create synthetic data sets that can essentially retrain older models on new data, creating a novel trajectory for the technology within contextual commerce and payments.
Aeropay Chief Revenue Officer Andrew Gleiser told PYMNTS last month that one use case he sees for generative AI is integrating it into merchant payment portals to surface compliant information to customers around their own best clients as it relates to metrics, including average order value, overall volume and purchase cadence, giving them greater insights to help both retain and acquire customers.
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