How payments architect Naresh Kumar Paturi is bringing AI into financial services the careful way — and open-sourcing the tools that keep money moving correctly underneath it.
Featuring Naresh Kumar Paturi — Principal Engineer & Architecture Lead, Payments and Distributed Systems
Ask most people what artificial intelligence will do to money, and you tend to hear something sweeping — robo-advisors, autonomous trading, banks without bankers. Ask Naresh Kumar Paturi, and the answer is quieter and far more exacting. After close to two decades building the systems that move money behind the scenes, he has a precise view of where AI belongs in finance, and an even sharper one of where it does not.
“AI in financial services must be practical, governed, and measurable,” he says. “It should solve real problems without creating uncontrolled risk.” That sentence is, in many ways, his whole philosophy of the moment — enthusiasm for what automation can do, paired with a hard insistence on the guardrails that make it safe. It is a philosophy earned the hard way, in a domain where the cost of being wrong is measured in other people’s money.
Paturi is a Principal Engineer and Architecture Lead specializing in payments and distributed systems. He spent more than a decade at PayPal working on settlement, reconciliation, and payment processing, and now helps modernize the core infrastructure at Greenlight, the family finance platform that helps parents teach children and teens about money. He has also, lately, begun releasing free, open-source tools that fix the small, expensive mistakes he has watched engineering teams make for years. Across all of it runs a single thread: trust — and how you build technology worthy of it, even as machines start making more of the decisions.
The careful case for AI in finance
The loudest conversations about AI in finance tend to imagine machines taking over judgment. Paturi’s interest runs in the opposite direction — toward the quiet, high-volume work where automation genuinely helps and the stakes are contained. A large share of what customers ask for, he points out, follows repeatable patterns: a question about a transaction, a request to understand a fee, a low-value dispute. Those can be interpreted, checked against the rules, and either resolved or routed — fast, consistently, around the clock.
“The best use of AI in fintech is not about replacing judgment,” he says. “It’s about helping the system respond faster and more consistently while keeping the right guardrails in place.” At Greenlight he has contributed to AI-driven workflows built on exactly that idea: read the customer’s intent, apply policy-based rules, resolve the routine cases automatically, and escalate anything higher-risk to a human — ideally with the context already gathered. The reported result has been a meaningful drop in customer-care calls and faster response times on common issues. The point, he is quick to say, is not the automation for its own sake. It is that people get helped sooner, and human agents are freed to spend their attention where it actually matters.
“The best use of AI in fintech is not about replacing judgment. It’s about helping the system respond faster while keeping the right guardrails in place.”
Why structure matters more than the model
If there is a single idea Paturi wants people to take from the current AI wave, it is that in financial services the architecture around a model matters as much as the model itself. In most consumer software, an assistant only needs to understand language. In finance, language is the easy part. To be useful and safe, an automated workflow also has to understand account context, transaction rules, risk thresholds, escalation paths, and compliance boundaries — because the same question can mean very different things depending on whose account it is and what the rules allow.
That is why he treats governance as non-negotiable rather than as an afterthought. “AI must be governed,” he says. “In fintech, you need authentication, audit trails, monitoring, escalation paths, and clear limits on what automation can decide.” An AI system, in his world, never runs free: it can interpret, suggest, and resolve within bounds, but the bounds are set by people, encoded in policy, and watched continuously. The underlying rule is absolute — you cannot allow automation to make unrestricted financial decisions.
“AI in financial services must be practical, governed, and measurable. It should solve real problems without creating uncontrolled risk.”
AI raises the stakes on getting the basics right
Here Paturi makes a point that cuts against the hype in a useful way: the more decisions you automate, the more it matters that the systems underneath are correct. An automated workflow that scores or resolves thousands of cases an hour is only as trustworthy as the data and the plumbing feeding it. If a webhook is forged, a number is rounded wrong, or a reconciliation quietly misses a discrepancy, AI does not fix that — it scales it. “An automated answer is only as good as the system that produced it,” he says. In other words, the arrival of AI makes the unglamorous foundations of finance more important, not less.
The part that never changes: reliability is trust
That conviction comes straight from Paturi’s core discipline. Long before the current AI moment, he was a reliability engineer — someone who believes that in distributed systems, failure is not an exception but an expectation, and that good systems are the ones designed to fail safely. Networks fail, databases slow down, messages arrive late, dependencies disappear. A well-built system assumes all of that and still provides a controlled, recoverable path.
“In fintech, reliability is not just a technical goal — reliability is customer trust,” he says. It is a line he returns to often, and it explains how he connects two things that can seem unrelated: the careful engineering of payment systems and the careful deployment of AI on top of them. Both are, in the end, about earning and keeping trust. Speed matters, he says, but “reliability is what makes speed sustainable.”
Open-sourcing the plumbing
Recently, Paturi has put that philosophy into public form. He has released three free, open-source tools that address persistent weak points in payments engineering — the exact foundations that an AI-driven future depends on. PayHooks verifies that incoming webhook events are genuinely from the provider they claim to be, using constant-time signature checks and replay protection. OpenRecon reconciles transactions across processors, gateways, and an internal ledger, keeping money in exact decimal arithmetic and surfacing the discrepancies that matter. PagePDF archives records as clean, timestamped PDFs for audit. Together they trace the lifecycle of a payment: ingest it safely, reconcile it correctly, archive it defensibly.
“Most teams rebuild the same fragile scripts for webhooks and reconciliation at every company, and small mistakes there move real money,” he says. “I wanted correct, auditable, dependency-free building blocks that anyone can read, trust, and adopt.” All three are released under a permissive license, carry zero third-party dependencies, and run entirely on the user’s own machine — no servers, no accounts, no data collection. The constraints are deliberate: code small enough to read end to end, with nothing hidden, is code you can actually trust.
Asked why he would give such work away rather than build a business around it, his answer is characteristically plain. The value he cares about, he says, is correctness becoming the default — and you don’t get there by locking it up. After a career spent learning how this work should be done, the most useful thing he can publish is a clear, correct reference that raises the floor for everyone.
“Small mistakes in webhooks and reconciliation move real money. I wanted building blocks anyone can read, trust, and adopt.”
Where this is heading
Put the pieces together and a coherent worldview emerges — one that is neither breathless about AI nor dismissive of it. Automation, in Paturi’s telling, is a powerful way to help customers and unburden teams, provided it is governed, measurable, and pointed at a genuinely human purpose. And it can only be as trustworthy as the systems beneath it, which is why he keeps returning to reliability, correctness, and now open, inspectable foundations.
It is a notably grounded vision for a technology so often described in world-changing terms. “Technology should reduce friction,” he says. “If AI helps a parent get support faster, helps a child learn about money, or helps a team focus on more complex problems, then it’s doing useful work.” In a financial world rapidly handing more decisions to machines, that may be the most important standard of all: not whether the technology is impressive, but whether, at machine speed, it can still be trusted.
ABOUT NARESH KUMAR PATURI
Naresh Kumar Paturi is a Principal Engineer and Architecture Lead specializing in payments platforms, distributed systems, card processing, reconciliation, reliability engineering, and AI-enabled workflows. With more than eighteen years of experience across fintech and payments, he has worked on high-scale financial infrastructure at companies including Greenlight and PayPal.He is the author of the open-source projects PayHooks, OpenRecon, and PagePDF — free, dependency-free tools for securing, reconciling, and archiving payments, available on GitHub at github.com/Naresh-Paturi-Community. His work centers on a single question: how do you build financial technology that is fast, secure, reliable, and trusted by millions of users?