The wraps are off a new Web-based virtual personal assistant (VPA) developed over the past two years by Spanish financial services provider, BBVA. Over the past two years, the 155 year old Spanish banker (founded in 1857 as Banco Bilbao Vizcaya Argentaria S.A.) has expanded its presence in 45 countries (leading share in Mexico and many South American countries). Its marketing, IT and customer care folks have been working with the computer scientists at SRI to introduce the “next generation” of VPA’s leveraging improvements in natural language understanding (across speech, chat, or text entry) with deep integration into the bank’s back office systems.
Greg Sterling, director of Opus Research’s Internet2Go program, and I were fortunate to be among the first to be briefed by Chief Innovation Officer Beatriz Lara Bartolomé and Gerardo Ponte Lorenzo from BBVA, joined by Bill Mark, VP of Information & Computer Sciences at SRI, who led the Lola Project, and Norman Winarsky, VP of Ventures at SRI, along with other members of SRI’s Lola Project development team. They delivered a demonstration of the Web-based VPA and how it fits with BBVA’s long-time strategy of creating “customer-centric” services. The key is “human-like understanding” of input from customers, regardless of whether they enter their instructions by talking or typing information into their computers.
As a key differentiator, Lola shows off the power of combining understanding and reasoning. The result, in the banking domain at least, is a highly-personalized resource that is able to understand a customer’s intention and then act upon that in a conversational mode. Beatriz emphasized that Lola is the foundation of BBVA’s efforts to build the bank of the future, both online and in the real-world by offering a more customer-centric vision. That vision is supported by over a decade of natural language understanding (NLU) resources from SRI – whose scientists developed the software that became Siri. Over the past two years, SRI continuously refined the hooks into BBVA’s back office systems so that responses to natural language queries could be fast and accurate.
With back-office queries working smoothly, the purpose of the internal launch is to collect more input from users and expand the corpus of expressions that the system can understand and act on. BBVA Compass (their US franchise) will be testing Lola over the next few months internally with employees and their families, and as the system continues to learn and develop over the next year, will be deployed more and more widely. BBVA’s intent, in the long run, is to apply the Lola’s powers of understanding and reasoning to enable Lola to act as an advisor to the banks customers regarding their finances and then make reasoned recommendations that really advance online banking.
BBVA and SRI worked closely to develop softare that is human-like and acts as an assistant. In the long run it is designed to know BBVA customers, what they want to do and then – in a way that is different from others – knows how to do those things and does them. That’s what BBVA means when it says that it is making its services “customer-centric.”
The demonstration was quite impressive. It showed how the service can accept spoken input or, when it makes more sense, let customers type in responses to information requirements. It was fast, accurate and was able to accomplish a number of banking tasks. The instantiation of Lola for BBVA will add new capabilities and support more actions over time as Lola learns banking over the weeks, months and years. The trick, from SRI’s point of view, is to get its core software to build its vocabulary and support a wider variety of transactions, which often requires more sophisticated search techniques to support low latencies on the back end.
Today, these efforts are being applied specifically to retail banking, where customers will benefit through Web sites. Smartphones, kiosks and drive through banks will soon follow for BBVA customers. For SRI, there are elements of Lola that are bound to be relevant to other vertical industries, including healthcare and education. As with most natural language understanding technologies, it will improve over time.
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