Inclusão Financeira e Dados Alternativos na Latam

Provenir e Credolab apresentam o novo e-book sobre a transformação pela qual a indústria Fintech passou em 2020 e como 2021 proporciona uma nova perspectiva positiva e desafiadora em relação à inclusão financeira. Por meio da experiência e competência de reconhecidos especialistas financeiros da região, buscamos entender e ter um melhor enfoque sobre como os bancos e empresas financeiras tradicionais têm reajustado suo scoring de crédito e requisitos burocráticos para fazer parte de uma nova forma de conceber a inclusão financeira e social.

Acesse nosso flipbook agora mesmo para explorar os insights que os especialistas do ecossistema Fintech da América Latina têm sobre um dos problemas mais importantes que o setor financeiro enfrenta: a inclusão financeira e o uso de dados alternativos.

  • Ignacio Carballo, Pesquisador e diretor do ecossistema de programas Fintech e Digital Banking, UCA
  • Marcel Van Oost,  Conselheiro e fundador da Fintech Startup com a colaboração de Marcial Gonzalez Fraga - Consultor e investidor Fintech
  • Clementina Giraldo, CEO & Founder, Dots & Tech
  • Bruno Diniz, Fintech Advisor, Managing Partner da Spiralem e Autor do livro “O Fenômeno Fintech”
  • Sebastián Olivera, Founder, Montevideo Fintech Forum e Co-Founder, We-Fintech – Red iberoamericana de mujeres

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What's inside?

Tecnologia que impulsiona a inclusão financeira

Explore histórias de mercado sobre dados alternativos e seu sucesso em melhorar a inclusão financeira.

Vitórias durante a pandemia COVID-19

Descubra o que funcionou e o que não funcionou para os líderes da região durante a pandemia.

Melhores práticas para 2021

Aprenda quais são as melhores maneiras de usar dados alternativos em seu benefício em 2021.

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Inclusão Financeira e Dados Alternativos na 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.