Capital One Innovation & Technology Culture

What practices does your team employ to foster innovation, and how have these practices led to more creative, out-of-the-box thinking?

Capital One’s legacy of innovation continues to influence our work. Internal tech events bring tech and non-tech associates together to experiment with new ideas and create the foundation for new products and services. The OnePatents program at Capital One also supports associates through the patent process, rewarding them for out-of-the-box thinking and recognizing them for their hard work.

How has a focus on innovation increased the quality of your team’s work?

Innovation has always been a focus at Capital One. The bank was the first U.S. financial institution to go all in on the public cloud. Capital One’s machine learning and AI capabilities are driving value for its customers and associates and the business — from fraud prevention to personalized customer experiences to the developer experience and much more. Beyond the tech landscape, bold ideas have led to the creation of Capital One Cafés, a new type of community banking experience.

How has a focus on innovation bolstered your team’s culture?

Innovation goes beyond everyday work at Capital One. Internal tech conferences give associates the opportunity to network and share more about their work. Plus, they’re able to learn more about the innovative work across Capital One and beyond. Less formal events, such as charity hackathons and even an e-games tournament, bring people together to share their passions outside of work and brainstorm how to bring those ideas to life at Capital One.

I don’t know anywhere else where you have personal flexibility and limited compromises when it comes to the tech stack. When I’m trying to convince people to come here, that’s what I tell them.

Describe a typical day with Capital One. What sorts of problems are you working on? What tools or methodologies do you employ to do your job?

I’m a ML engineer for the enterprise platforms and technology team. Our primary focus is solving complex ML problems that help our colleagues, who in turn assist Capital One customers in becoming more financially empowered.

We help teams solve problems using models we create from Capital One’s existing store of datasets. Our work can range from developing predictive time series models to implementing natural language processing. For me, most of this work happens using Python for data processing, internal platforms for orchestration of our applications and agile methodologies to help structure all of our work.

Share a project you’ve worked on that you’re particularly proud of. 

One of the projects I’m proud of is helping to build a suite of anomaly detection and monitoring tools that help us identify suspicious or anomalous activity involving financial transactions.

Product managers, data scientists and ML engineers came together to build models that better allow us to service our customers, such as more quickly mitigating fraudulent activity.

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

My team is responsible for investing in emerging research in the AI and ML space and applying it to the financial context. We do this by working closely with our tech partners and teams across Capital One to identify common modeling challenges within the business. Additionally, we accomplish this through applied experimentation to identify the best AI and ML techniques to solve a particular business challenge and scale the impact so it can be used across the company.

Our team has helped introduce unsupervised and graph ML techniques to support our customers and the business in various areas, such as identifying fraud, customer service needs and providing personalized app experiences. This work has helped reduce the time data scientists spend on customized feature engineering, allowing them to move quickly and efficiently deliver value to customers sooner.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

My team works closely with our partners to deliver on Capital One’s AI and ML strategy, which includes thinking about responsible applications of AI. Across all of our work, we’re guided by a mission to build and deploy AI and ML in a well-managed way that puts people first. This includes working cross-functionally across the business to follow best practices, such as extensive testing and implementing human-centered guardrails before introducing AI systems or models into any customer or business setting. 

We also ensure that we only use AI and ML to solve a problem when it’s the right way to solve the problem. We see that, in some situations, business logic is sufficient enough to solve the problem at hand. We focus our efforts toward making an impact on our business rather than trying to use something flashy just for the sake of saying we use AI.

Can you share some examples of how AI and ML has directly contributed to enhancing your product line or accelerating time to market?

By partnering with teams focused on fraud, we’ve been able to use ML techniques to make big breakthroughs in our ability to mitigate fraud and help protect our customers. Using AI and ML, we’re better able to adapt to emerging changes in behavior patterns as fraudsters constantly change their techniques, greatly improving our detection of anomalous behavior. We’re continuing to experiment with new AI and ML capabilities to stay at the leading edge of this space and continue to give customers the best possible experience.