The payments industry is now too large for slow routing assumptions. It generates $2.5 trillion in revenue and is supported by 3.6 trillion transactions, which means a small delay inside a routing layer can become visible at enormous scale. For banks, merchants, and consumers, the problem is not abstract. It is the checkout that spins, the business payout that stalls, or the callback that arrives late enough to trigger a support case. The industry has modernized real-time payment products faster than the systems that route them.
Ritvik Pandya, a Software Engineering Manager at a major global financial institution, has worked on real-time payment platforms where low latency and reliability are operating requirements. His standing as a Raptors Fellow reinforces that systems judgment, especially in a field where standards, performance, and practical reliability often meet under pressure. To understand why global payment rails break under load, we turned to Pandya for a closer view of routing design and what comes next.
When Real-Time Becomes the Baseline
“A payment rail does not fail loudly at first. It starts with a slow callback, a queue that should have drained faster, and one tail-latency chart that makes everyone stop talking,” says Pandya. “That is the moment when routing stops being plumbing and becomes the product.” His point is simple. In real-time payments, users experience the system through speed, confirmation, and trust, not through the elegance of the underlying design.
Real-time payments accounted for 266.2 billion transactions globally in 2023 after growing 42.2% year over year, and that volume changes the engineering standard. Pandya’s payment initiation work addressed this pressure by moving away from a legacy proxy path toward more direct downstream routing, with connection pooling, asynchronous processing, and resource tuning used to reduce P95 latency by about 25 to 30 percent. Such a latency gain in a low-volume workflow is useful; in real-time payments, it can protect reliability guarantees when peak traffic pushes the slowest requests into view.
The Multi-Rail Problem Is Really a Consistency Problem
Once latency is under control, the harder question is whether a platform can keep its behavior consistent across rails that do not look alike. Financial institutions are expanding across multiple real-time payment rails simultaneously, and those rails carry distinct regulatory, processing, and latency requirements. They are also tied to downstream systems that do not always fail in the same way. The world now has 80 countries with live RTP networks, which makes global payments a practical routing problem rather than a slogan.
Pandya’s work on the payment initiation platform supported roughly 3 to 5 rail and workflow families, including examples such as UPI, Brazil DD, Canada RTR, QR-based payment initiation, and mandate lifecycle workflows. The approach was not to treat every rail as a special case forever. He pushed for a unified entry point and standardized integration patterns so those payment and adjacent workflow families could move through the same platform without forcing every downstream system into the same shape.
“The mistake is pretending every rail is identical,” Pandya says. “The better approach is to standardize the parts that should be shared, then isolate the parts that carry regional or operational risk.”
Deterministic Routing Replaces Hope With Locality
With more rails connected, the next failure mode is usually hidden inside the routing layer. Centralized routing systems built on legacy proxy layers can become the performance ceiling when every transaction competes for the same generalized path. Cross-node communication overhead adds latency and inefficiency. Uneven load distribution makes the problem worse. In a real-time rail, those tail-latency spikes can undermine the reliability guarantees the product is supposed to provide.
The payment orchestration platform market was valued at $1.7 billion in 2024 and is projected to reach $6.1 billion by 2030, reflecting demand for routing layers that can manage more complex payment paths. Pandya’s work replaced generalized routing behavior with deterministic, hash-based node affinity that enforces data locality, so related transactions are consistently handled by the optimal processing node. In the broader platform program, that routing discipline helped raise throughput toward roughly 1.8 to 2 times prior levels without making deterministic routing alone carry every performance claim. His HackerNoon publication, “Deterministic Routing: The Hidden Key to Low Latency,” fits naturally here because it turns a production lesson into a broader engineering argument. Routing choices decide where the work lives.
Isolation Is How Platforms Stay Fair Under Load
Routing locality helps, but it does not fully solve contention. Shared infrastructure contention can still create tail-latency spikes when one busy flow crowds out another. The risk grows when synchronous payments and asynchronous QR or mandate workflows share the same platform. Nobody wants that pager at 2 a.m. when the root cause is not a code defect but a noisy neighbor.
The container orchestration market is projected to reach $1.24 billion in 2030 from $688.113 million in 2025, a useful signal for teams that need controlled deployment, scaling, and networking of distributed application components. Pandya’s implementation used dedicated Kubernetes node-group isolation per payment flow, paired with multi-threaded asynchronous processing models and optimized worker pools. That separation reduced noisy-neighbor issues, supported synchronous and asynchronous workloads within the same environment, and helped avoid performance degradation across mixed payment flows. The same infrastructure efficiency program reduced waste enough to contribute to a six-figure annual savings range, tying isolation to cost discipline rather than only incident response.
“The goal is not just to add more nodes,” Pandya says. “The goal is to make sure the right workload gets the right capacity at the right time, without stealing predictability from another flow.”
The Future Belongs To Purpose-Built Routing
The larger lesson is that payment speed cannot be solved only at the edge. Faster rails require purpose-built routing decisions that are aware of locality, isolation, downstream capacity, callbacks, and regional constraints, not another generalized proxy layer. The instant payments market shows why this discipline will matter more, with transaction value projected to move from $60 trillion in 2025 to $129 trillion in 2030, a 115% rise over the period. At that scale, inefficient routing becomes expensive in several ways at once: latency, infrastructure waste, production noise, and client confidence.
Pandya’s platform work also integrated roughly 3 to 4 downstream execution and callback systems, keeping payment initiation, execution, status handling, and callback behavior connected across mission-critical flows. This is the distinct client-side scale proof for the closing section, separate from the latency, rail breadth, throughput, and savings metrics used earlier. His role as a Beta: AI Super Hackathon judge also fits the broader moment, because modern engineering leaders are increasingly asked to evaluate systems under messy, concurrent, real-world conditions rather than clean lab assumptions. For payment platforms, the call is direct: build routing that respects how money actually moves.
“The future of real-time payments will not be won by adding another proxy layer,” Pandya says. “It will be won by knowing where each transaction should go before the system is already under stress.”
