This text is delivered to you by Capital One.
After 5 years main pure language understanding and finally the complete Alexa AI group at Amazon, Prem Natarajan made a nontraditional transfer: He turned Chief Scientist at a financial institution. Not simply any financial institution: Capital One, a monetary establishment serving over 100 million clients, serving to on a regular basis Individuals handle their monetary lives.
For Natarajan, a veteran of DARPA-funded analysis and academia who had watched machine studying evolve from task-specific purposes to basis fashions, the logic was clear. A few of the most fascinating advances in AI analysis and deployment have been shifting from large tech’s horizontal platforms to business verticals like finance, the place probably the most complicated 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 appropriate place to do it. For many years, the corporate has been acknowledged as one of the data- and analytics-driven monetary establishments within the business. Its enterprise mannequin from the very starting was constructed round utilizing information and know-how to personalize monetary merchandise for purchasers. A decade in the past, Capital One went all in on the cloud and rebuilt its information ecosystem, making a unified atmosphere for information, compute, and AI and machine studying experimentation. At this time, its fashionable infrastructure, disciplined method to governance, and deep bench of expertise kind the inspiration that enables it to guide in enterprise AI.
Advances in AI analysis and deployment are shifting from large tech’s horizontal platforms to business 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 know-how to deploy – leveraging the most recent giant language mannequin, deploying it by means of APIs, and integrating it into current workflows – fairly than a scientific self-discipline. Capital One is doing one thing completely different: constructing a scientific group and analysis group to unravel real-world buyer issues and invent impactful AI options that don’t but exist.
Whereas broadly accessible basis fashions can deal with common duties, they will’t but clear up many domain-specific challenges, similar to detecting fraud in real-time throughout billions of transactions, or offering state-of-the-art conversational instruments so clients can interact when, how, and the place they wish to.
These challenges of constructing AI dependable, scalable, and effectively ruled require unique analysis and scientific innovation that’s funneled again into the enterprise to create real-world purposes to deal with buyer wants.
The Constraints That Demand Innovation
Prem Natarajan, an IEEE Fellow, is Chief Scientist at Capital One. “If you wish to clear up actually necessary issues in AI and see your work come to life, this is among the few locations you are able to do that,” he says.Capital One
As a result of banks are coping with folks’s funds, there may be an extremely excessive bar for getting it proper in terms of AI. Take fraud, for instance. Even a minor fraud occasion can have a devastating affect on sure clients. The very best 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 clients and their monetary info with accuracy and pace. all these challenges, Capital One and Natarajan noticed that serving hundreds of thousands of consumers meant fixing AI issues at a scale and complexity that many enterprises don’t encounter. These similar constraints create a singular analysis atmosphere.
At Capital One, the method to constructing AI is to offer worth to clients 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 large scale and world-class threat administration necessities, makes the scientific issues each more durable and simply as consequential as these present in most large tech labs.
Advancing AI By means of “Vacation spot-Again Considering”
Capital One’s method to AI analysis and innovation begins with what Natarajan calls “destination-back considering.” Reasonably than asking what’s doable with present know-how, the staff envisions the shopper expertise they wish to ship – maybe a automotive 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 quick, personalised steerage – after which works backward to determine the scientific breakthroughs required to get there.
“You’re considering again from the place you’re offering extremely useful companies,” Natarajan explains. “After 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 staff has already recognized what’s going to make a tangible distinction in clients’ lives.
However methodology alone isn’t sufficient. Capital One’s almost 15-year guess on cloud-first structure created one thing uncommon in monetary companies: a unified information and compute ecosystem that may assist the form of scientific experimentation sometimes seen in large tech analysis labs. As the one main U.S. financial institution to go all-in on public cloud infrastructure, Capital One eradicated the legacy programs that may constrain AI analysis at most monetary establishments. This contemporary tech stack permits speedy iteration, large-scale mannequin coaching, and what Natarajan calls “steady studying,” programs that enhance after deployment fairly than degrading over time. This distinctive method to infrastructure is a crucial element in making new classes of analysis doable.
Agentic AI: From Analysis to Manufacturing
The analysis agenda manifests in programs already serving clients. Early final 12 months, Capital One launched what often is the first absolutely agentic AI customer support expertise constructed solely in-house by a financial institution: a automotive shopping for instrument that takes actions on behalf of consumers based mostly on their requests, not simply solutions questions. Behind it lies intensive analysis into multi-agentic AI reasoning programs that may navigate real-time information, enterprise data, constraints, and guardrails, with varied brokers that may work collectively to perform complicated duties.
Capital One has launched a totally agentic AI customer support expertise powered by intensive analysis into multi-agentic reasoning programs that may navigate real-time information.
The staff 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 hundreds of thousands in initiatives that span psychological well being, supplies discovery, science, know-how, engineering, and arithmetic training, human-AI collaboration, and drug improvement.
Within the spring of 2026, the corporate hosted its inaugural AI Symposium to deepen connections and foster insight-sharing between the scientific AI group, main AI labs, startups, and its personal know-how, 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 worldwide chief in AI innovation for 3 consecutive years, noting the financial institution accounted for 38 % 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 staff – 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 clear up actually necessary issues in AI and see your work come to life, this is among 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 purposes like excessive frequency buying and selling, it’s utilizing science to boost monetary experiences for over 100 million on a regular basis Individuals, 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 programs that may dramatically enhance efficiency by reframing how issues are solved, and domain-specific reasoning that understands contextual and monetary nuance – symbolize the following section of innovation. “By simply casting the issue in an agentic framework, you may truly get far more efficiency” from the identical underlying fashions, he explains.
It’s this sort of utilized analysis, like translating common capabilities into manufacturing programs for hundreds of thousands of consumers, that defines the Chief Scientist’s mandate. When recruiting expertise to his AI staff, a gaggle comparable solely to probably the most refined tech corporations 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 programs that remodel monetary companies for hundreds of thousands of on a regular basis Individuals – that’s altering the world. And it requires the form of scientific rigor that solely a Chief Scientist can lead.
