Money used to move on a banker’s schedule. It now moves on the customer’s. That shift has been building for years, and the numbers show how far it has gone: 266.2 billion real-time payment transactions were recorded globally in 2023, a jump of 42.2% over the prior year. Behind that growth sits a harder story that rarely reaches the headlines. The systems that authorize, enforce limits on, and settle those payments were mostly built for a slower world, and someone has to rebuild them without turning off the lights.
Vishal Shah has spent much of the last decade on exactly that problem. A Senior Engineering Manager with more than 22 years in software, he leads the modernization of core payment platforms at a major U.S. bank, where his teams rebuild the services that decide, in a fraction of a second, whether a transaction is allowed to proceed. He is also a Senior Member of the IEEE, recognizing significant professional achievement and technical leadership. His focus is narrow and unglamorous: make the plumbing faster, steadier, and ready for volumes that keep climbing.
When the Old Payment Core Runs Out of Room
A large U.S. bank can process tens of millions of payment transactions in a single day, spread across instant transfers, bill pay, card networks, and wires. For years, the decision-making behind those payments lived in fragmented rule engines, some of them decades old, each with its own logic for permissions and limits. That arrangement held up when payments settled in batches overnight. It strains badly when a customer expects a transfer to land in seconds and a fraud check to clear just as fast.
Shah’s approach started with separation. He applied domain-driven design to pull permissions, spending limits, and real-time decisioning apart into distinct services with clean boundaries, so a change to one no longer risked breaking the others. His teams rebuilt these as independent microservices, mapped the integration contracts between them, and defined a shared security model so that instant transfers, ACH, bill pay, and wires all evaluated risk the same way. The hardest part was consistency. A customer who taps a button twice should not pay twice, so his teams built idempotent APIs and event-driven reconciliation that let the system retry safely and settle to one correct answer even when a network hiccup or a downstream slowdown got in the way. The goal was not a novelty. It was giving a tangled system a shape that engineers could actually reason about and extend.
“You cannot modernize what you cannot explain,” Vishal Shah says. “Half the work is drawing clear lines around each responsibility. Once the boundaries are honest, the speed and the reliability follow.”
Engineering for Speed at Enormous Scale
Demand for instant money movement is not slowing. Real-time account-to-account payment volumes rose 40% worldwide in 2024, and these systems now handle roughly a quarter of all digital retail payments. Every point of that growth lands as pressure on the machinery underneath: more transactions to evaluate, tighter latency budgets, and no patience for a decision that arrives a second late.
Shah’s platform now supports more than 40 million payment decisions a day, each one checked against permissions and limits before the money is allowed to move. To hold latency down at that volume, his teams kept frequently used data in memory close to the services that needed it and backed it with highly available data stores, then tuned concurrency so the system stayed steady during peak load rather than buckling. The rebuild increased throughput capacity by 20–30% and cut engineering cycle time by roughly a quarter, which meant new payment features reached customers sooner. Stronger observability and automated failover also brought high-severity incidents down by 15% to 20%, a number the operations staff on call felt as keenly as any customer did.
“At this scale, latency is the product,” Shah notes. “People feel a slow payment the way they feel a dropped call. The engineering job is to make the fast path the normal path, every time, for everyone.”
When Reliability Becomes the Whole Point
In real-time payments, a few minutes of downtime is not an inconvenience. It can strand hundreds of thousands of in-flight transactions and send customers straight to a call center. Reliability targets in this world are measured in fractions of a percentage point, and the gap between 99% and 99.9% availability is the gap between hours and minutes of outage over a year.
On a companion modernization effort, Shah’s team reached 99.9% uptime by running services across highly available clusters with automated scaling and health checks, then caught problems early through distributed tracing and structured logs rather than after customers complained. That emphasis on measurable outcomes also extends beyond his engineering work. He also judges the Business Intelligence Group’s Sales and Marketing awards, scoring entries from companies across the industry on evidence and outcomes rather than polish. Reviewing other people’s claims, he says, sharpens how he documents his own.
“Uptime is a promise you make to people who never see your code,” Shah reflects. “They only notice it when you break it. So you design as if someone is always watching, because in payments, someone always is.”
The Security Layer Behind Every Transaction
The same speed that customers love also rewards fraud. Account takeover, where a criminal seizes a legitimate account, drove more than $15.6 billion in reported U.S. losses in 2024, up from $12.7 billion the year before. Faster payments give stolen credentials less time to be caught, which puts real pressure on the checks that run in the milliseconds before money leaves an account.
Shah has spent years on that boundary. On an earlier program, he led the cloud architecture for a voice authentication system that verified a customer’s identity from the sound of their voice during a call, evaluated it in real time, and flagged likely fraud before a risky request went through. It replaced knowledge-based questions, the old “name your first pet” checks that a determined attacker can research or guess, with a signal that is far harder to fake. Getting there meant stitching together the phone system, the servicing applications, the biometric engine, and the fraud-scoring backend so they agreed on a single verdict in real time, with encrypted storage for the voiceprints themselves. Across millions of calls a year, it shortened verification for genuine customers while raising the wall in front of everyone else.
“Security only works when it disappears for the right person and appears for the wrong one,” Shah observes. “The moment protection becomes a hassle for honest customers, they route around it, and then you have neither speed nor safety.”
Building the Foundation for What Comes Next
The next round of efficiency is coming from inside the engineering process itself. On Shah’s teams, AI-assisted tools now handle a growing share of routine scaffolding, code generation, and documentation, work that once ate hours of senior engineers’ time. That change helped trim roughly a quarter off delivery cycles and freed people to spend their attention on the hard architectural calls that tools cannot make. Senior engineers, in his view, earn their keep when they are wrestling with tradeoffs, not typing boilerplate.
Shah is deliberate about where those tools get pointed. He treats AI as a way to remove repetitive effort rather than replace judgment, and he holds generated code to the same review and testing bar as anything a person writes. The reusable service templates and shared components his teams built for one payment platform now speed up the next one, so each modernization leaves the ground more prepared for the one that follows. The through-line across permissions, reliability, security, and tooling stays the same: build systems that other engineers can trust and extend long after he has moved on.
“The measure of good infrastructure is boring,” Shah explains. “No drama at 2 a.m., no surprises. If the next team can build on what we left without cursing our names, then we did the job right.”
