Five insights into how Capital One is gaining momentum with enterprise AI – VentureBeat

“We always start with our obsession with the end customer and how we can use AI to solve their problem; that is the unifying starting point,” Arjun Dugal, executive vice president, divisional CIO, Card Technology at Capital One, told the audience at VB Transform. Dugal explained how the company’s strategic plans start with customer and stakeholder value and then work back into defining the specifics of how to achieve them.

Capital One’s strong customer centricity anchors how they evaluate every use case of AI and defines their relative priority as part of the financial services giant’s broader strategic plan. For their latest fiscal year, they reported $36.8 billion in revenue, up 7.4% from the previous year.  

It is key to stay focused on that vision while ensuring the latest AI and machine learning technologies contribute to greater compliance, successful integration of generative AI (genAI) and traditional AI models, and human-in-the-loop AI practices.

Being data-driven is in Capital One’s DNA

Capital One founders Richard Fairbank and Nigel Morris introduced the idea of using statistical analysis to create customized credit card offers for different customer segments in 1987. Capital One relied on a data-driven approach to differentiate itself in the growing credit card services market. Capital One was the first credit card company to go all in on advanced data and statistical analysis.

Having founded the company on a data-driven approach, Capital One’s accuracy and speed in deciphering massive amounts of customer and market data has given them an edge in risk management, defining new services and gaining nuanced customer insights not otherwise available.

Dugal’s many insights shared into how Capital One is making the most of AI and machine learning during the fireside chat he participated in at VB Transform reflects how mature the financial services firm is in being selective about how they integrate new technologies into their business.

To gain greater speed and scale, Capital One has doubled down on creating a modern data ecosystem, starting with revising its tech stack. Next, it has built an in-house technology team of 14,000 people and is democratizing AI/ML across every part of our business with well-managed, human-in-the-loop processes.

Dugal emphasized the importance of human-in-the-loop as a core part of their approach to all forms of modeling, from ML to generative AI pilots now in progress. Five core insights come from Dugal’s points shared at VB Transform.  

Five Insights

The following are the five key insights Dugal shared into how Capital One can accelerate the company’s AI capabilities and stay ahead in the enterprise race:

Balancing rapid advancement with compliance. Staying in compliance with regulatory requirements by region and nation is one of the most challenging areas of running a large-scale financial services busness. Capital One has taken on this challenge by having compliance reporting, analysis and prediction alongside their AI initiatives to make sure they both benefit each other. In essence, Capital One is looking to learn how to accelerate compliance and turn it into a competitive advantage using AI.  Dugal emphasized the need to have regulatory-compliant AI frameworks in place that can scale globally and deliver needed insights in real-time to eliminate any delays.

Integrating generative AI and traditional AI models. It’s evident from Dugal’s remarks that Capital One is actively investing in both genAI and machine learning models, with one of the main goals being to democrate AI-based decision-making across the company. By integrating genAI with existing machine learning models, Capital One looks to unlock more insights into the growing amount of data their daily operations produce. Integrating genAI and traditional AI models need to meet the high regulatory standards of quality and security, which are table stakes in a highly regulated industry like banking.

Focusing on Human-in-the-Loop AI applications. Dugal emphasized how Capital One prioritizes human-in-the-loop AI applications to reduce risk, ensure accuracy, and enable models to learn in real time from human contextual intelligence.  This approach ensures that while AI can scale to handle routine tasks reliably, human oversight is maintained for more critical decision-making processes.

Building a prepared technological ecosystem. Capital One continues to make significant investments in its modular technology infrastructure that allows AI to be integrated without extensive rework or tech stack overhauls. This includes leveraging public cloud infrastructure for scalable AI model training and ensuring data management practices focus on excelling at maintaining data quality​. Capital One was the first U.S. bank to go all-in on the cloud, which is a testament to its commitment to creating and continually refining its technological ecosystem. Transitioning to the cloud enabled the company to create a unified enterprise platform for software delivery, fostering an environment conducive to rapid innovation and experimentation while maintaining necessary governance and consistency.

Continually Investing in Talent Development. Dugal emphasized how crucial a role talent development is in any AI initiative throughout his discussion at VB Transform. To build and grow a strong AI-capable workforce, Capital Onne focuses on both internal talent development and external recruitment. Focusing on the goal of scaling AI across their global operations, Capital One sees talent development as the catalyst needed to scale operations using the latest AI applications they’re building. Dugal noted that Capital One has also been ranked as a top bank for AI talent, reflecting its successful strategies in attracting, retaining, and upskilling top AI professionals.

Conclusion

Taking on the challenges of balancing growing compliance, regulatory requirements, and increased global competition, Capital One’s all-in approach to integrating AI into its operations serves as a model for other large-scale financial institutions. Dugal’s comments and perspective reflect the maturity Capital One has when it comes to evaluating gen AI and its potential contributions to its customers, employees, and business.