Capital One’s Human-Centered Approach to AI
How Capital One Achieved Scalable Enterprise AIAdd bookmark
From the very beginning, Capital One served as a bellwether for data and analytics. Upon its founding in 1994, the firm quickly made a name for itself as a new kind of consumer business, one built around “information-based strategy,” and a pioneer in using analytics to understand consumer spending patterns. It was even the first company to name a Chief Data Officer in 2002.
Since then, Capital One has continued to push the boundaries of data-driven innovation. Like many companies, back in 2011, it embarked on a multi-year digital transformation journey to amplify its use of advanced analytics and pave the way towards enterprise artificial intelligence (AI).
As Capital One's SVP of Technology, Chris Nims, explained at a recent conference “Capital One recognised early on that the winners in banking of the future will be great tech companies with the risk management skills of a leading bank. They will build great customer experiences, delivered in real time, through software, data, and algorithms. We sought to completely redefine who we are as a company, to build a technology company that does banking, instead of a bank that just uses technology. We needed to become great at building software. And we needed the top engineering talents to do it.”
To support the development of cutting-edge technology, in 2011, Capital One launched its innovation incubator a.k.a. The Lab. Whereas a traditional automation COE is, by design, somewhat siloed off from the rest of the organization and serves as a “strategic partner” to the rest of the business, The Lab strives to be strategically integrated with the business units. In addition, it also frequently engages with those at the forefront of innovation, academic institutions and AI researchers. They’ve even opened satellite incubators on college campuses in Maryland and Illinois.
To elaborate on its approach to innovation, here’s a look at some of Capital One’s recent AI-related accomplishments..............
Meet Eno, Capitol One’s Intelligent Assistant
A multi-talented, AI-powered virtual assistant, Eno helps Capitol One members more effectively manage and protect their accounts from malicious parties. Similar to voice assistants such as Siri and Alexa, users can ask Eno simple customer service questions like “what is my account balance?” or “how do I deposit a check?”.
In addition, Eno also functions as a fraud alert tool. If a suspicious charge is made, Eno will notify the customer and request verification. Eno also includes a virtual card number (VCN) feature. Using machine learning (ML), the VCN tools provide users with a unique credit card number for each online merchant they frequent. That way, A.) customers don’t have to worry about their primary credit card # being stolen and, B.) if fraudulent charges are made to the VCN down the line, CapitalOne can more effectively identify where and why it was compromised.
As Margaret Mayer, Capitol One’s MVP, Software Engineering wrote on the company’s blog, “Put simply, we want to ensure that humans are at the forefront of every decision that goes into the AI/ML lifecycle. From a macro perspective, the autonomous systems we develop and deploy should positively contribute to our customers’ lives--helping them spend more time living and doing what means the most to them, not more time banking.” By providing customers with a helpful, conversational, efficient, and trustworthy interaction, Eno does just that.
AI-Based Crypto Fact Checker
Effectively trading cryptocurrency is harder and much riskier than it looks. A highly volatile market, it requires almost constant monitoring as the markets operate 24/7. Furthermore, the cryptocurrency world is especially prone to scams and misinformation given its unregulated, wild west atmosphere.
To help crypto traders more accurately and efficiently analyze potential transactions, Capitol One has developed a new AI program designed to verify the credibility of the cryptocurrency-related information in real-time.
In a nutshell, the program works by first scanning various potential news sources (i.e. social media, medium posts, crypto news sites) for relevant information and noteworthy events. This information is then fed into a machine learning powered “credibility analysis engine,” which cross-references and determines whether the info is credible based on historical examples. If the information is credible, it then considers how the market has responded in the past.
As they explained in the June 2020 patent application, “The machine-learning algorithm can also determine the reach … and how quickly the news spreads out, what investors said and felt … on social media as the news was spreading out, how long it took for the initial fear, if any, to fade out, for the ‘buy-the-bottom’ mood to arise, as well as for the market to bounce back up.”
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Historical data is dead. As global corporate enterprises restructure their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI- putting 'math on top of data' - all make that goal possible. Join us for lessons learned from those who are accomplishing the goal.
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Historical data is dead. As global corporate enterprises restructure their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI- putting 'math on top of data' - all make that goal possible.
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