BBVA Insights adds a self-driving dimension to online and mobile banking

“‘BBVA Insights is an exciting functionality that we are delivering to BBVA USA customers in mobile and online,’ said BBVA USA Head of Retail Customer Solutions Manolo Moure. ‘With BBVA Insights, customers can let AI-powered technology do the driving, with proactive insights that are automatically created when their linked accounts show activity that doesn’t fit their usual patterns. Customers will be better able to manage their financial health without additional heavy lifting.’”

How artificial intelligence gives ecommerce a boost

  • “Artificial intelligence (AI) is not only a tool to help improve the user experience and increase customer satisfaction, it can also facilitate payment management and detect potential fraud.
  • The data also reveals that during the first three months of the pandemic, 4 percent of these consumers were new to online shopping in general and 8.3 percent used online shopping for the first time to buy food products.”

Technological highlights from 2019 and a look at the year ahead (Vol. II)

    • “Yesterday BBVA published five interviews with some of the banks tech leaders asking what they thought were their highlights for the past year and what to look out for in 2020.
  • How will this impact the way in which customers and clients access financial services in the future? 
    • In terms of specific areas of impact, I think that we will see a lot in the true personalization of the experience, and also in making financial services more and more transparent and easy to use. In the internal process side we will also see a lot in the usage of AI and RPA (robotic process automation) to make them much more efficient and lean.”

    Elena Alfaro

    Global Head of Data & Open Innovation in BBVA Client Solutions

    “Last year, BBVA worked with a team of MIT researchers to develop a model based on machine learning algorithms that can reduce the number of false positives related to fraudulent credit card transactions by 54 percent. The new approach, known as deep feature synthesis (DFS), facilitated the extraction of more than 200 additional attributes from each transaction, which served to provide a more detailed description of the credit/debit card transaction behavior, thus improving the fraud detection engine results.”