The global financial system has always drawn hard lines around who gets to participate. Those lines are written in numbers, in credit histories, in documents that some people never had the chance to gather. But if those lines were erased and rewritten with context, intent, and a measure of human judgment, what would financial access look like?
That question had been quietly sitting in Anshuman Chowdhury’s own notes for years. In 2019, he mapped out a personal roadmap—one that included a future at Meta, followed by the launch of A3Z AI. He had written it down, then forgotten it. It wasn’t until months after the unexpected layoffs that he stumbled across the diary again—and realized he was right on schedule.
A3Z AI, founded in mid-2023 by Anshuman Chowdhury, emerged from that long-term question made urgent by lived experience: how do you build trust into the world’s most exclusionary systems? Instead of following the path laid by big data platforms or legacy compliance rails, the company is building a different kind of foundation—one grounded not in mass data collection or surveillance logic, but in trust. Not trust as branding, but trust as infrastructure.
Built from Conversations, Not Spreadsheets
Anshuman Chowdhury didn’t draw up his business plan inside a corporate accelerator or pitch deck war room. He wrote it in the margins of his life — between Uber rides, on the sidelines of youth sports tournaments, and during long, searching talks with people who had been rejected again and again by systems that never saw them clearly.
“A3Z isn’t a product that came from spreadsheets,” Chowdhury says. “It came from listening to people who didn’t have a seat at the table, who kept being told no by systems that weren’t built for them.”
That’s not a metaphor. It’s how the company’s earliest version was drafted: not by chasing market trends, but by mapping the pain points of the underbanked, the underserved, and the financially invisible. These aren’t users who failed the system. These are individuals the system never tried to serve. Chowdhury, who spent years leading products at Meta, Amazon, and Visa, had seen what global-scale infrastructure looks like. What he wanted now was to make systems that didn’t default to exclusion.
So instead of designing another tool to plug into legacy compliance models, A3Z AI built a modular, explainable, and behavioral scoring engine. It doesn’t ignore data. It asks better questions about it. The system pulls from a different vocabulary: contextual spending, behavioral intent, user volatility, and trust signals too subtle for a bank statement to show. In pilot deployments, the results were impossible to ignore. Users who would normally be denied credit saw a 300 to 400 percent increase in approval when A3Z’s model was used instead of bureau-only scores.
Trust as the Starting Point
Traditional financial infrastructure asks people to prove themselves before they are allowed in. A3Z flips that expectation. It starts with a basic assumption that people are more than the sum of their paperwork. From there, it applies a structured, interpretable model to map trust, not through blind faith but through behavioral logic.
“You can’t build inclusion by copying the same rules that excluded people in the first place,” Chowdhury says. “You have to rewrite what counts as credible.”
The company’s scoring system doesn’t depend on credit cards, mortgage histories, or a trail of official transactions. Instead, it identifies behavioral patterns that suggest stability, intent, and financial responsibility — even when those behaviors come through rent payments, mobile recharges, or consistent peer-to-peer transfers. It does so in a way that’s readable by both the institution and the user. There’s no black box. Every decision can be explained.
That model is already showing promise in early trials. Through a friends-and-family launch in select UK communities, it’s being used to assess first-time property buyers with no existing credit scores. In the US, it’s being tested with renters and gig workers often excluded from traditional lending channels. While the current focus is on fintech and proptech applications, Chowdhury envisions extending the platform to support talented youth athletes from underserved markets, helping them establish identity and qualify for early-stage funding and travel sponsorships. He’s already in discussions with academy partners in the UK and India to pilot the system.
While the product architecture is modular and technically advanced, its mission is deeply human. A3Z AI doesn’t want to score people; it wants to recognize them.
Different from the Start
Every startup says it’s different. A3Z didn’t need to say it. It showed it. From the beginning, Chowdhury made it clear that the company wasn’t going to play the same game as its better-funded peers. He knew what Plaid, Alloy, and Nova Credit were building: fast, powerful pipelines for onboarding, KYC, and lending. But he also knew what they were missing.
“Plaid’s about access to data. Alloy is great at orchestrating rules. Nova Credit brings in outside files. But nobody’s asking what the user wants to be seen for,” he says. “That’s where we live.”
The company’s tools are built for what Chowdhury calls “trust scaffolding.” Partners don’t just get an API. They get a framework for evaluating people with thin files and high intent. The product stack allows real estate platforms, sports academies, and fintech startups to verify identity, assess creditworthiness, and process onboarding without forcing users through hoops they were never meant to jump.
This approach hasn’t gone unnoticed. NGOs focused on financial access, think tanks studying ethical AI, and even a few regulators have begun reviewing A3Z’s models. The modular structure is easy to deploy, but more importantly, it’s hard to ignore. When a company can cut onboarding times from 12 weeks to three while increasing approval rates, partners take notice.
Still, A3Z isn’t chasing publicity. Its site is sparse, and its founder rarely tweets. The work happens in small rooms, not big stages. That, according to Chowdhury, is exactly the point.
“People don’t need another keynote. They need products that believe in them before anyone else does.”
The Long Game Is Human
Ask Chowdhury where he’s going with all this, and he won’t name a revenue target or valuation milestone. He’ll tell you about a kid in Africa trying to prove his identity to play sports abroad. He’ll mention a single mother in India trying to get approved for her first home. These are the use cases that keep him building. These are the reasons he plans to file a provisional patent for the dignity-preserving credit algorithm, because for the people he’s building for, nothing short of systemic recognition will do.
He’s not trying to be the next Stripe or Square, and he’s not racing toward acquisition. A3Z isn’t a software company with a sleek user interface. It’s a platform for trust, engineered by someone who knows what it means to be underestimated and has no intention of forgetting it.
What Chowdhury has built isn’t flashy. But it might be the most necessary product in fintech right now: a system that doesn’t just ask who you are, but listens hard enough to hear the answer.
Photo Courtesy of: Anshuman Chowdhury
