FinTech veteran Silvio Tavares was appointed president and CEO of VantageScore Solutions and will bring his expertise from the Digital Commerce Alliance (DCA), a banking industry association he founded and operated as CEO and chairman, according to a press release on Tuesday (Oct. 12).
Tavares also served as a senior executive at Visa and Fiserv/First Data, where he advanced both companies’ data and information products while also tapping financial information to decrease risks while increasing revenue. At DCA, Tavares headed a global trade association with top brand members like Bank of America, MasterCard, Discover, Klarna, Microsoft and others.
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His range of expertise includes driving innovations in data and analytics, managing environmental, social and governance (ESG) initiatives as well as inclusion and stakeholder management.
Aside from co-authoring more than 12 patents in financial data analytics, Tavares holds a Juris Doctor from Boston University, an MBA from Boston College, and a BS in Electrical and Computer Engineering from Tufts University.
He said there are great opportunities at VantageScore and that he will work with the team to leverage the company’s current momentum while also targeting “innovation, data-driven outcomes and financial inclusion.”
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He added that the company will also continue offering lenders “predictive credit scoring models” that work across all ranges.
“Having a more inclusive financial system has been linked to stronger and more sustainable economic growth and development,” said Sid Singh, president of United States information solutions at Equifax. “Equifax and VantageScore have a shared commitment to addressing the needs of underserved communities.”
Tavares is taking the reins from Vantage founding president and CEO Barrett Burns, who is moving on to the newly created position of vice chairman. Burns is responsible for making VantageScore part of the mainstream consumer lending ecosystem and launching access to free credit scores. He was also instrumental in the development of models to score people who are ‘credit invisibles’ under traditional models.
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