Envestnet to unveil product to help banks avoid an SVB scenario
Tech company accelerated development of the product, which tracks bank deposit flows nationwide
Envestnet is rushing to release a product Monday that is designed to help financial institutions avoid a “Silicon Valley Bank situation.”
The wealth tech giant’s new Bank Deposit Index will allow bank treasury executives to track their own inflows or outflows of deposits, and contextualize these relative to banking segments by “big, regional or small banks,” as well as consumer segments within certain states.
The index, which began development the day before SVB’s collapse on March 10, tracks approximately 15% of households in the U.S., 6% of annual banking deposits, and 80 million daily transactions across more than 2,000 institutions, according to Envestnet.
To develop the index, Envestnet pulled from its real-time, anonymized national consumer database, extracting net deposit flows using algorithms to categorize the information into a dashboard for data viewing via Amazon Web Services or data platform Snowflake. The Bank Deposit Index is designed to deliver insights to bank executives’ dashboards using Envestnet’s API integrations, said Farouk Ferchichi, president of Envestnet data and analytics.
“The client is going to log in into the secure environment in our own operated cloud, and they’re going to be able to access the dashboard,” Ferchichi said. “A secure pipeline between us and the [financial institutions] is how we’re going to trade APIs.”
Regulators did not contact Envestnet about the development of the product. Ferchichi said that only certain regulators may request access to the index data, and that “if the regulators do not come to [Envestnet] directly and ask,” Envestnet would not offer them access to it.
Envestnet quickly pushed the product to market because of SVB’s downfall last Friday. After the product’s launch, Envestnet plans to add functionalities, such as inflow and outflow alerts specific to a given bank, and deposit data on national, regional and state levels, Ferchichi said.
“We built a self-learning algorithm that is being developed as we speak, to cut through all the noise for a [bank] president and can focus them on a particular consumer segment in a particular region behaving in a certain way,” he said.
