Capital One, like many other traditional banks, used rule-based, deterministic tools to monitor transactions for money laundering. Machine learning for anti-money launderingĪs per the United Nations Office on Drugs and Crime (UNODC) estimate, 2.7 percent of global GDP is being laundered. This enabled them to reduce the turnaround time for resolving incidents by up to 50%, thereby giving Capital One a major competitive advantage. So, Capital One’s ML team created an inference engine by integrating these siloed datasets from different monitoring tools and linking it with appropriate metadata to help engineers identify the failed component with its reason. Monitoring tools capture these components’ health but store that data in a siloed manner. Capital One mobile app is essentially a complex interconnected system built as a mesh of APIs, microservices, databases, and compute & storage infrastructure. To achieve the same, Capital One has created an ML-based intelligence layer on top of its industry-leading monitoring tools observing mobile app failures. This requires the firm to be highly responsive in resolving technical failures to minimize credibility loss in such an eventuality. Using ML for quick turnaround during mobile app failureĬapital One Mobile App is an integral part of the firm’s strategy to deliver seamless, digital services to its millions of customers. These models have enabled Capital One to seamlessly extend their VCN to a large number of online sites thereby improving response times while preserving customer privacy and security. Figure 2 –Eno’s VCN providing safety and quick turnaround Capital One’s Eno team solved this problem by creating two models one detects when the customer is on a payment page so that the Eno browser extension can be popped up, and the second classifies input fields within the payment page so that the Eno browser extension can automatically fill in the appropriate payment information. However, non-standardized HTML markups on payment pages of different merchant sites make it necessary for Eno’s rules to be configured for each merchant site. Eno browser is required to identify payment pages and fields using rules and autocomplete payment processor patterns. Each VCN mapped to a specific merchant provides safety to customers against original physical card details being leaked in case of a breach. While making online purchases, the Eno browser extension binds a unique virtual card number (VCN) to a specific merchant in lieu of the customers’ physical card numbers. This unlocks a critical network effect of customers training Eno with more data about their intentions, which in turn improves the algorithms, leading to lesser frauds and a better, seamless customer experience.Įnhancing user experience around virtual card numbers through EDGE ML Eno uniquely communicates these decisions proactively and allows customers to respond in their own terms and words. Eno automatically alerts customers about potential frauds in real-time, and if required, locks their card. One of the recent additions to Eno is a fraud detection engine. It helps customers check their bank balances, track purchases, pay bills online, connect with spending, and proactively monitor their accounts and keep them secure.įigure 1 – Multi-channel support provided by Capital One’s Eno (personal banking assistant) Real-time, Intelligent, Automated Customer ExperiencesĮno by Capital One, was the banking industry’s first, natural language SMS chatbot that has evolved into a multichannel solution with numerous capabilities. Today, Capital One applies AI/ML to almost every facet of its business from customer-facing personal assistants, to call center operations, and even pre-emptive maintenance of its digital assets apart from credit decisioning. Capital One’s holistic AI-powered approach It was the first bank to appoint a chief data officer in 2002 and by 2011, was conducting 80,000 big data experiments a year.Ĭapital One has leveraged these historical strengths to become an AI-first company in the past ten years and transformed itself into an agile, cloud-native, large-scale engineering organization. As a diversified bank with more than 65 million customers, Capital One has been a pioneer in digital technologies. was founded as a credit card company in 1988, it has focused on leveraging data analytics to understand consumer spending patterns.
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