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Inclusión Financiera y Datos Alternativos en LATAM

Provenir y Credolab presentan el nuevo eBook sobre la transformación que atravesó la industria Fintech en 2020 y cómo 2021 plantea una nueva perspectiva positiva y desafiante respecto a la inclusión financiera. A través de la experiencia y competencia de reconocidos expertos financieros en la región, buscamos comprender y tener un mejor enfoque sobre cómo los bancos tradicionales y las compañías financieras han reajustado su scoring crediticio y sus requisitos burocráticos para formar parte de una nueva forma de concebir la inclusión financiera y social.

Accede a nuestro flipbook ahora mismo para explorar los insights que los expertos en el ecosistema Fintech de América Latina tienen sobre uno de los problemas más importantes que enfrenta la industria financiera: la inclusión financiera y el uso de datos alternativos.

  • Ignacio Carballo, Investigador y director del ecosistema de programas Fintech & Digital Banking, UCA
  • Marcel Van Oost,  Asesor y Founder de Fintech Startups con colaboración de Marcial Gonzalez Fraga, Fintech Investor
  • Clementina Giraldo, CEO & Founder, Dots & Tech
  • Bruno Diniz, Asesor Fintech, managing partner en Spiralem y autor de “El fenómeno Fintech”
  • Sebastián Olivera, Founder, Montevideo Fintech Forum y Co-Founder, We-Fintech – Red iberoamericana de mujeres.
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What's inside?

Tecnología que impulsa la inclusión financiera

Explore historias del mercado sobre datos alternativos y su éxito en la mejora de la inclusión financiera.

Victorias durante la pandemia COVID-19

Descubra qué funcionó y qué no funcionó para los líderes de la región durante la pandemia.

Mejores prácticas para 2021

Descubra cuáles son las mejores formas de utilizar datos alternativos para su beneficio en 2021.

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Inclusión Financiera y Datos Alternativos en 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.