Financial Inclusion and Alternative Data in LATAM

Provenir and Credolab present to you a flipbook on the transformation the fintech industry went through in 2020 and how 2021 begins with a positive and challenging new perspective with regards to financial inclusion. Through the expertise and proficiency of recognized financial experts in the region we are looking to understand and have a better approach on how traditional banks and financial companies have readjusted their credit scoring and their bureaucratic requirements in order to become part of a new way to conceive financial and social inclusion.

Access the flipbook now to know what the best experts in Latin American fintech ecosystem have to say about one of the most important issues facing financial industry right now during these challenging times, financial inclusion and the use of alternative data.

  • Ignacio Carballo, Research Economist and Director Fintech Ecosystem & Digital Banking at UCA
  • Marcel Van Oost,  Financial Advisor and Fintech Startup Founder with collaboration of Marcial Gonzalez Fraga, Fintech Investor
  • Clementina Giraldo, Dots & Tech CEO & Founder
  • Bruno Diniz, Fintech Advisor, Managing Partner at Spiralem and Book Author: “The Fintech phenomenon”
  • Sebastián Olivera, Montevideo Fintech Forum Founder and WeFintech Co-Founder, the Iberoamerican Women Network
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What's inside?

Technology Driving Financial Inclusion

Explore stories from the market on alternative data and its success in improving financial inclusion.

Wins During the COVID-19 Pandemic

Find out what worked and what did not work for the leaders in the region during the pandemic.

Best Practices for 2021

Learn what are best ways to use alternative data to your benefit in 2021.

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Financial Inclusion and Alternative Data in LATAM

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CredoLab is at the forefront of innovative risk management practices that engage with novel credit risk modelling approaches availed by the surge in cell phone use. Core to CredoLab’s business is its modelling pipeline. Taking the smartphone as input, the data processing pipeline consists of a series of automated steps, rooted in machine learning techniques, that ultimately outputs a predictive model for credit default. To protect the confidentiality and to ensure against bias towards individual loan customers, only non-identifying metadata is used.

This e-book reports the findings of Dr Xiaofei (Susan) Wang, Lecturer and Research Scholar, Yale University from a review she did on CredoLab’s scoring model. She considered a vast array of alternative approaches for the various different steps of the pipeline and found favourable results, including when applied to real data.

In this e-book, we first explore the data sets that CredoLab consumes, how it translates it into scores, and the outcome it serves. In the latter part of the paper, we take a look at how CredoLab’s algorithm fared when compared to that of other major players with similar scoring models.

Dr. Xiaofei (Susan) Wang, PhD

Lecturer and Research Scholar, Department of Statistics & Data Science, Yale University

Born in Nanjing, China, Dr. Wang moved to the USA at an early age and has been associated with some of the leading institutions. She did her bachelors from the University of California and her PhD in Statistics from Yale University. She currently holds esteemed positions at a number of associations and works at Yale University as a lecturer and research scholar. She has a number of publications and accolades to her credit.