Over the last few years, I have spent more time inside payment systems than outside them. When you see millions of transactions move through a platform every day, you start noticing patterns that most people never get to see. You see how the system self-corrects during a brief dip, how fraud signals shift by the minute, and more. As we head into 2026, these patterns are no longer edge cases; they are shaping the baseline expectations for modern financial systems.
This past year, what stood out even more was the growing intensity of these moments. Festival peaks, sudden shifts in user behaviour, new demand patterns from merchants, and entirely new categories of traffic that did not exist a year ago. India’s digital payments landscape does not give systems the luxury of slowing down. The pressure on reliability, speed, and precision has never been higher, and the tools that served us five years ago, no longer scale at the pace the ecosystem now expects.
What changed silently underneath all of this is the growing depth of AI inside these systems. It fills the space between manual checks, keeps up with patterns that move too fast for traditional rules, and supports decisions that need to be made in milliseconds. These shifts, taken together, point to an important shift for next year. As we look ahead, a few themes clearly stand out, each signalling where the next phase of fintech innovation will come from.One shift is already visible across the ecosystem. Agentic AI is becoming part of the infrastructure itself. At the same time, these agents are increasingly visible closer to the user. Instead of a single system making isolated decisions, multiple specialised agents now operate in parallel, handling tasks such as customer support, guiding payment or purchase choices, and resolving issues in real time. By distributing decision-making across agents, platforms reduce friction for users while making complex systems feel faster and easier to navigate.
With the global AI agents market projected to reach more than 103.6 billion dollars by 2032, growing close to 45 percent annually, the direction of movement is clear. Their ability to act on context, measure outcomes, and improve autonomously marks a turning point in how fintech systems will operate at scale.Running parallel to this shift is the rise of multimodal intelligence. For years, AI systems largely operated in silos, interpreting either structured data, or text, or logs. Financial systems, however, do not behave in single modes. A risk decision can involve transaction patterns, user behaviour, device fingerprints, screenshots, and compliance notes, all interacting at once. Multimodal models bring these signals together, creating a deeper understanding of events across the stack.
With the global multimodal AI market expected to grow more than 35 percent annually, the adoption curve will be steep. Fintech platforms that combine multimodal reasoning with domain-specific financial models will build products that interpret context more accurately and reduce ambiguity in decision-making.
This intelligence becomes even more crucial when we look at how fraud is evolving. Fraud patterns today shift faster than rule-based engines can adapt, driven in part by attackers using automation and generative models to mimic legitimate behaviour at scale. The rise in digital fraud losses over the past year reflects this acceleration. Analysts estimate that from 2023 to 2027, cumulative online payment fraud losses worldwide will cross 340 to 360 billion dollars. Static defences cannot compete with adversaries that iterate continuously. The move to real-time behavioural modelling is already showing results, reducing false positives, shortening investigation cycles, and flagging anomalies within milliseconds. As attacker systems become more autonomous, fraud prevention engines will need to mirror that adaptability. In 2026, the sophistication of a fintech platform’s fraud intelligence will be one of its clearest competitive differentiators.
These shifts in intelligence and adaptability are also redefining expectations from real-time payment systems. India continues to lead global thinking here. With daily UPI transactions expected to cross one billion by 2027, infrastructure must handle concurrency and settlement at a scale few ecosystems have managed before.
But throughput alone is no longer enough. The rails must be intelligent, able to predict traffic surges, optimise routing in real time, scale automatically, and maintain precision to the final decimal. As digital commerce expands across borders, these expectations are now extending beyond domestic payments. Businesses and consumers increasingly expect cross-border settlements to approach the speed and transparency of local payments, even as compliance, and regulatory complexity intensify.
The opportunity lies in unifying AI-led risk checks, automated compliance, real-time settlement intelligence, and transparent forex management into systems that can scale globally without adding friction.
The impact of AI is also visible in how fintech products are built and integrated. As platforms become more modular and API-driven, engineering productivity is shifting from writing code faster to resolving integration complexity faster. AI-native tools trained on financial domain knowledge are shortening feedback loops across development. They help teams navigate integrations more easily by clarifying which APIs to use, how different components fit together, and where potential issues may arise early. In a space where reliability depends heavily on clean integrations, this support reduces friction long before systems go live.
As fintech ecosystems expand, the quality of a developer’s experience, especially for external teams integrating with these platforms, will directly influence how quickly businesses can experiment, launch, and scale. In 2026, strong developer tooling will be less about convenience and more about infrastructure resilience.
All of this ultimately shapes how users experience financial products. Personalisation is no longer a feature but a baseline expectation. Users increasingly expect systems to adapt in real time, whether through contextual credit decisions, relevant prompts, or payment flows that adjust to past behaviour. AI enables this at scale by learning continuously from behavioural, transactional, and contextual signals. In a market like India, this adaptive layer will be central to building trust, driving retention, and sustaining long-term engagement.
Across industries, we are entering a phase where intelligence will be treated as fundamental infrastructure. It will shape everything from how products are built to how decisions are taken to how organisations respond to uncertainty. The shift is not about moving toward a future without human oversight, but augmenting it at scale. As systems grow more capable, our responsibility will be to ensure that this intelligence is deployed with clarity, governance, and long-term thinking. The choices we make now will determine the resilience of the systems we rely on for the next decade.
The author is Ramkumar Venkatesan, Chief Technology Officer, Cashfree Payments.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
