Staff Directory

Maria Fernandez Vidal

Senior Financial Sector Specialist

Maria Fernandez Vidal is a Senior Financial Sector Specialist, leading CGAP’s work on Data. Her work is focused on the role data can play in advancing financial inclusion and covers a range of topics including data-driven business models and open finance. She has also worked substantially with fintechs, platforms and advanced analytics. Before joining CGAP, Maria was at McKinsey & Company, working in Latin America & the Caribbean and the US. At McKinsey, she served clients in the financial sector and the public and social sector on a broad range of topics including strategy, risk, operations and organization. Maria previously worked at the Inter-American Development Bank (IDB), the International Monetary Fund (IMF) and Endeavor.

Maria holds an MBA from The Wharton School at the University of Pennsylvania with majors in strategic management and finance, and graduate and undergraduate degrees in economics from the Universidad Torcuato Di Tella.

By Maria Fernandez Vidal

Research

Is Data Privacy Good for Business?

Do poor customers value data privacy? Six experiments in India and Kenya indicated they do and are willing to pay for it. For providers, this suggests that offering products with privacy and protection features can give them a competitive market edge.
Blog

Algorithm Bias in Credit Scoring: What’s Inside the Black Box?

Computers can make faster, better, and less biased lending decisions than humans, but only if human bias hasn’t crept into their algorithms.
Research

Data-Driven Segmentation in Financial Inclusion

Financial services providers can improve their businesses by using segmentation to develop a more accurate understanding of their customers. This guide shows providers how they can use data analytics to understand their customers by performing more complex analyses and extracting insights that were previously hidden.
Research

Using Satellite Data in Financial Inclusion

Financial services providers that see an opportunity to reach financially excluded people in rural areas can use new technology to remotely gather and analyze data on potential customers. This guide explains foundational concepts of machine learning and how FSPs can apply those methods.
Research

Credit Scoring in Financial Inclusion

Statistical models can help lenders in emerging markets standardize and improve their lending decisions. This guide emphasizes that the effectiveness of data analytics approaches often involves building a broader data-driven corporate culture.