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After 5 years main pure language understanding and ultimately all the Alexa AI group at Amazon, Prem Natarajan made a nontraditional transfer: He grew to become Chief Scientist at a financial institution. Not simply any financial institution: Capital One, a monetary establishment serving over 100 million prospects, serving to on a regular basis People handle their monetary lives.
For Natarajan, a veteran of DARPA-funded analysis and academia who had watched machine studying evolve from task-specific functions to basis fashions, the logic was clear. A few of the most fascinating advances in AI analysis and deployment have been shifting from huge tech’s horizontal platforms to trade verticals like finance, the place probably the most advanced issues aren’t simply constructing fashions however making AI work underneath the constraints of real-world buyer issues, contextual enterprise data, steady studying, with an extremely excessive bar for accuracy and privateness.
That’s additionally what made Capital One the suitable place to do it. For many years, the corporate has been acknowledged as one of the vital data- and analytics-driven monetary establishments within the trade. Its enterprise mannequin from the very starting was constructed round utilizing information and expertise to personalize monetary merchandise for patrons. A decade in the past, Capital One went all in on the cloud and rebuilt its information ecosystem, making a unified surroundings for information, compute, and AI and machine studying experimentation. Right this moment, its trendy infrastructure, disciplined method to governance, and deep bench of expertise type the muse that permits it to guide in enterprise AI.
Advances in AI analysis and deployment are shifting from huge tech’s horizontal platforms to trade verticals like finance.
So, why does a financial institution want a Chief Scientist? The reply lies in a elementary false impression about AI in monetary companies. Most monetary establishments nonetheless view AI as a expertise to deploy – leveraging the most recent massive language mannequin, deploying it by APIs, and integrating it into current workflows – slightly than a scientific self-discipline. Capital One is doing one thing completely different: constructing a scientific neighborhood and analysis group to unravel real-world buyer issues and invent impactful AI options that don’t but exist.
Whereas broadly obtainable basis fashions can deal with normal duties, they’ll’t but resolve many domain-specific challenges, reminiscent of detecting fraud in real-time throughout billions of transactions, or offering state-of-the-art conversational instruments so prospects can have interaction when, how, and the place they wish to.
These challenges of constructing AI dependable, scalable, and effectively ruled require authentic analysis and scientific innovation that’s funneled again into the enterprise to create real-world functions to handle buyer wants.
The Constraints That Demand Innovation
Prem Natarajan, an IEEE Fellow, is Chief Scientist at Capital One. “If you wish to resolve actually essential issues in AI and see your work come to life, this is likely one of the few locations you are able to do that,” he says.Capital One
As a result of banks are coping with individuals’s funds, there’s an extremely excessive bar for getting it proper in the case of AI. Take fraud, for instance. Even a minor fraud occasion can have a devastating affect on sure prospects. The perfect fraud fashions and platforms can detect and assist mitigate fraud within the time it takes somebody to faucet their card, which is desk stakes for safeguarding prospects and their monetary data with accuracy and pace. these kind of challenges, Capital One and Natarajan noticed that serving tens of millions of shoppers meant fixing AI issues at a scale and complexity that many enterprises don’t encounter. These identical constraints create a novel analysis surroundings.
At Capital One, the method to constructing AI is to supply worth to prospects in methods by no means doable earlier than, bettering their monetary lives and assembly them the place they’re with companies they really want. That focus, mixed with huge scale and world-class threat administration necessities, makes the scientific issues each more durable and simply as consequential as these present in most huge tech labs.
Advancing AI Via “Vacation spot-Again Considering”
Capital One’s approach to AI research and innovation begins with what Natarajan calls “destination-back considering.” Quite than asking what’s doable with present expertise, the workforce envisions the client expertise they wish to ship – maybe a automobile purchaser who works lengthy days and may solely analysis the choices at 10 p.m., or a buyer dealing with an sudden expense who wants rapid, personalised steering – after which works backward to establish the scientific breakthroughs required to get there.
“You’re considering again from the place you’re offering extremely useful companies,” Natarajan explains. “Upon getting that imaginative and prescient clearly, you’re employed again and say, what are the gaps? What are the issues we have to invent?” This ensures that when issues are solved, the affect is actually assured, as a result of the workforce has already recognized what is going to make a tangible distinction in prospects’ lives.
However methodology alone isn’t sufficient. Capital One’s practically 15-year wager on cloud-first structure created one thing uncommon in monetary companies: a unified information and compute ecosystem that may assist the type of scientific experimentation usually seen in huge tech analysis labs. As the one main U.S. financial institution to go all-in on public cloud infrastructure, Capital One eradicated the legacy methods that may constrain AI analysis at most monetary establishments. This contemporary tech stack permits fast iteration, large-scale mannequin coaching, and what Natarajan calls “steady studying,” methods that enhance after deployment slightly than degrading over time. This distinctive method to infrastructure is a vital part in making new classes of analysis doable.
Agentic AI: From Analysis to Manufacturing
The analysis agenda manifests in methods already serving prospects. Early final yr, Capital One launched what stands out as the first absolutely agentic AI customer support expertise constructed fully in-house by a financial institution: a automobile shopping for software that takes actions on behalf of shoppers primarily based on their requests, not simply solutions questions. Behind it lies intensive analysis into multi-agentic AI reasoning methods that may navigate real-time information, enterprise data, constraints, and guardrails, with varied brokers that may work collectively to perform advanced duties.
Capital One has launched a completely agentic AI customer support expertise powered by intensive analysis into multi-agentic reasoning methods that may navigate real-time information.
The workforce can also be engaged on fixing issues like tokenization challenges, defending delicate information whereas enabling mannequin coaching. To speed up this cutting-edge work, Capital One has established partnerships with Columbia College, the College of Southern California, and the College of Illinois, and have become the one financial institution funding NSF’s nationwide AI analysis facilities in 2025, investing tens of millions in initiatives that span psychological well being, supplies discovery, science, expertise, engineering, and arithmetic schooling, human-AI collaboration, and drug growth.
Within the spring of 2026, the corporate hosted its inaugural AI Symposium to deepen connections and foster insight-sharing between the scientific AI neighborhood, main AI labs, startups, and its personal expertise, science, and AI leaders and companions.
Constructing a World-Class AI Group
Exterior validation suggests the technique is working. Evident AI ranked Capital One because the main financial institution in AI expertise and a world chief in AI innovation for 3 consecutive years, noting the financial institution accounted for 38 p.c of all AI patents filed by the highest 50 monetary establishments. Capital One was additionally acknowledged by IFI Insights as the one monetary establishment among the many high U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, IBM, Microsoft, Intel, Adobe and Samsung. Capital One’s AI workforce – which has expertise from main AI labs and high universities – represents experience not often discovered outdoors Silicon Valley.
However recruitment requires a mission. “If you wish to resolve actually essential issues in AI and see your work come to life, this is likely one of the few locations you are able to do that,” Natarajan says. The pitch is constant: Capital One isn’t simply optimizing algorithms for area of interest monetary functions like excessive frequency buying and selling, it’s utilizing science to boost monetary experiences for over 100 million on a regular basis People, increasing engagement and real-time insights, personalization, and entry to their private funds and merchandise like by no means earlier than.
Capital One was acknowledged as the one monetary establishment among the many high U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, and Microsoft.
The frontiers Natarajan is most enthusiastic about – agentic AI methods that may dramatically enhance efficiency by reframing how issues are solved, and domain-specific reasoning that understands contextual and monetary nuance – characterize the subsequent part of innovation. “By simply casting the issue in an agentic framework, you’ll be able to really get far more efficiency” from the identical underlying fashions, he explains.
It’s this sort of utilized analysis, like translating normal capabilities into manufacturing methods for tens of millions of shoppers, that defines the Chief Scientist’s mandate. When recruiting expertise to his AI workforce, a gaggle comparable solely to probably the most subtle tech firms in caliber, Natarajan frames the chance round a mission. He invokes Steve Jobs’ well-known problem to John Sculley: “Do you wish to spend the remainder of your life promoting sugared water, or do you wish to change the world?” For Natarajan, the parallel is evident. Constructing AI methods that remodel monetary companies for tens of millions of on a regular basis People – that’s altering the world. And it requires the type of scientific rigor that solely a Chief Scientist can lead.
