Every time a card clears in under a second, the transaction now passes through layers of artificial intelligence (AI, software that learns patterns from data) and cloud-native code that did not exist in most banks five years ago. AI-driven payment routing, cloud-based gateways and machine-learning fraud checks have quietly become the plumbing of modern banking, and the engineers rebuilding that plumbing rarely make the news.
Customers notice the chatbots and the redesigned apps. Far less visible is the transaction layer underneath, where technology leaders such as Vasanthy Vijay, who works on AI-powered banking platforms and payment gateway integration, spend their time. That layer is where the harder economics of modern banking now get decided.
A Payment Layer Moving Record Volume
The pressure on that layer is a volume story first. Real-time payments (RTP, bank transfers that settle in seconds rather than days) reached 266.2 billion transactions globally in 2023, a year-over-year jump of 42.2%, according to a global real-time payments forecast from ACI Worldwide. The same study projects 575.1 billion transactions by 2028, a compound annual growth rate of 16.7%.
India alone recorded 129.3 billion of those 2023 transactions, more than any other market. By 2028, real-time rails are expected to account for 27.1% of all electronic payments worldwide and to add roughly 285.8 billion dollars to global gross domestic product (GDP, the total value of goods and services an economy produces).
Volume on that scale changes what a bank’s back end has to survive. A single festival weekend or payday spike can throw tens of millions of extra transactions at systems that were designed when batch processing overnight was normal. The routing engine has to pick a path in milliseconds, and the core ledger has to confirm it without falling over.
Why Legacy Core Systems Hit a Wall
Most of those ledgers were not built for this. Many banks still run core systems up to 40 years old on mainframe hardware and outdated programming languages, and 55% of banks name the limits of their existing core as the single biggest roadblock to hitting their business goals.
The cloud has not solved this quietly in the background. More than 90% of retail banks worldwide use cloud computing somewhere, but adoption for the core itself stays low, because 69% of institutions cite the perceived risk of migrating live accounts as a barrier. Only about a third believe they can respond to market changes in the time the market actually allows.
The fix the industry has converged on is cloud-native architecture paired with an API-first approach (API, application programming interface, the connectors that let separate software systems talk to each other). One documented modernization cut processing costs by 82%, lifted availability from 98.7% to 99.99%, and raised throughput capacity 8.5 times. The contrast between the two models is stark.
| Attribute | Legacy core | Cloud-native core |
|---|---|---|
| Scaling for volume spikes | Fixed capacity, manual provisioning | Elastic, scales on demand |
| Time to ship a change | Months, full release cycles | Days, continuous deployment |
| Integration with fintech | Custom point-to-point connectors | Open APIs, reusable |
| Typical availability | Around 98.7% | Up to 99.99% |
This rebuild is the foundation everything else rests on, which is why the Federal Reserve research briefing on core banking modernization treats the core as a systemic question rather than a vendor choice. Without it, the smart routing and AI fraud tools have nothing solid to run on.
How AI Reroutes Every Transaction
Once the foundation is cloud-native, the routing layer becomes the place AI earns its keep. Modern payment ecosystems push transactions across many gateways and acquiring banks, and a machine-learning model (ML, software that improves its predictions by training on past data) can pick the route most likely to succeed. Research on AI smart routing reports that authorization rates climb from about 87% to 94% or higher, with overall success rates rising as much as 10% when the model weighs hundreds of parameters against millions of past payment data points.
The factors an AI router weighs in that fraction of a second include several signals at once:
- The issuing bank’s recent approval behavior for similar cards
- Each gateway’s live health and latency
- Time of day and the bank’s processing windows for retries
- Cost per route, since processors charge different rates
- The probability a given acquirer will accept the specific transaction
Cost is the other half of the equation. In a pilot with more than 20 large companies including eBay and Microsoft, one payment processor’s AI routing saved participants an average of 26% on processing costs. The underlying methods are documented in an open paper, including an academic study on AI-powered smart routing for payment systems that lays out how the acceptance-probability model is trained.
Fraud Moved Faster Than the Old Defenses
The same automation that speeds up legitimate payments has armed the attackers. AI-enabled fraud, including deepfakes and synthetic identities, surged 1,210% between January and December 2025, against a 195% rise in traditional fraud. Deloitte projects that generative-AI fraud losses could reach 40 billion dollars in the United States by 2027, and global banking fraud losses are estimated to climb from 23 billion dollars in 2025 to 58.3 billion in 2030, a rise of roughly 153%.
Banks have responded by pushing AI into the defensive line, where it now runs anomaly detection, automated reconciliation and cloud-enabled compliance checks across the transaction stream. The measured results sit on the other side of the ledger:
- 90% of financial institutions now use AI for fraud detection
- 42% of card issuers say AI saved them more than 5 million dollars in blocked fraud over two years
- 83% of industry leaders report AI cut false positives and the customer churn they cause
- The share of attempts classified as advanced rose from about 10% in 2024 to 28% in 2025
The defensive value is concrete and documented, including in Mastercard’s account of AI in payment fraud prevention. The catch is that both sides are now improving at machine speed, so the defense never gets to stop.
Where Engineers Like Vasanthy Vijay Fit In
None of this ships itself. It takes engineers who can hold the AI model, the cloud architecture and the regulatory rulebook in the same head. Vasanthy Vijay’s described work over recent years sits at exactly that intersection, helping financial institutions move off fragmented legacy infrastructure toward scalable, cloud-native systems that can run real-time operations.
The job spans several distinct workstreams that have to fit together:
- AI-powered payment processing that adds speed and security to transaction handling
- Cloud-native gateway architecture built on API-first integration
- Intelligent payment routing tuned for higher success rates and lower cost
- AI-based monitoring, anomaly detection and automated compliance
- Microservices and API connectivity that let banks plug in fintech partners
That blend is rare, which is why a wave of banks are restructuring around it rather than hiring a single specialist. The shift toward intelligent financial infrastructure is forcing organizations to redesign how their teams work, a theme explored in coverage of how AI-native banking is driving a full structural reset.
The Bill for Modernization Comes Due
The upside here is real, and so is the cost. Rebuilding the payment layer is expensive, slow and risky, and the failure modes land on live customer money rather than a test environment.
Migration is where most of the fear concentrates, given the 69% of institutions that flag it as a barrier. Spending alone does not guarantee a return either. Many banks bolt AI onto existing processes instead of redesigning them, which is a recurring reason that AI returns in banking keep slipping through institutions’ hands.
The macro picture confirms the gap. The 2025 McKinsey Global Payments Report finds most banks have not yet turned AI into efficiency gains at scale, while the minority that have are pulling ahead on speed to decision and loss-rate performance. The divide is no longer about who adopts AI; it is about who rebuilt the plumbing underneath it first.
If a bank rewires its core and routing layer over the next few years, real-time volume and machine-speed fraud become problems it can absorb. If it keeps layering tools on a 40-year-old ledger, the same volume and the same fraud become the reasons it loses ground, one cleared second at a time.
Frequently Asked Questions
What Is AI-Driven Payment Routing?
AI-driven payment routing is software that uses machine learning to choose the best path for each transaction across multiple gateways and acquiring banks in real time. By analyzing hundreds of parameters against past payment data, it lifts authorization rates from roughly 87% to 94% or higher and can improve overall success rates by as much as 10%.
Why Are Banks Moving to Cloud-Native Payment Gateways?
Banks are moving to cloud-native gateways because legacy cores cannot scale for real-time volume or ship changes fast enough. More than 90% of retail banks already use the cloud in some form, and 55% name their existing core as the biggest barrier to their goals; documented migrations have cut processing costs by up to 82% and raised availability to 99.99%.
How Does AI Improve Fraud Detection in Banking?
AI improves fraud detection by spotting anomalies and patterns across millions of transactions faster than rule-based systems. About 90% of financial institutions now use AI for fraud detection, 42% of issuers report saving more than 5 million dollars in blocked fraud over two years, and 83% of leaders say it reduced false positives.
What Is a Cloud-Based Payment Integration Gateway?
A cloud-based payment integration gateway is a hosted system that connects a bank to payment networks, processors and fintech partners through open APIs rather than fixed point-to-point connectors. It supports real-time payments, multi-channel processing and cross-border transactions while scaling automatically during demand spikes.
Are Real-Time Payments Growing Globally?
Yes. Global real-time payment transactions reached 266.2 billion in 2023, up 42.2% year over year, and are forecast to hit 575.1 billion by 2028. By that year they are expected to make up 27.1% of all electronic payments worldwide, with India the single largest market.
What Skills Do Banking Payment Modernization Engineers Need?
These engineers need a blend of artificial intelligence, cloud computing, payment gateway integration and enterprise financial architecture, plus a working grasp of compliance and fraud risk. The combination matters because the AI model, the cloud system and the regulatory rulebook all have to function together on live customer money.
