Can Crypto Lending Drive Financial Inclusion?

Decentralised finance — or DeFi — includes all the financial elements you know from more traditional finance (such as central banks, retail banks, lenders and borrowers). But a key difference to traditional finance is that DeFi has no centralised institution like a central bank and sovereign nation that underpins the whole system.


This alternative financial system holds the promise to be truly global, open-source and accessible to all who have access to the internet, regardless of nationality, ethnicity, race, gender and socioeconomic class.


The era of blockchain-based innovation has the potential to improve diversity and inclusion, an area where finance, and fintech, have long performed dismally.

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Cryptocurrency: Helps or Hinders Financial Inclusion?

For better or worse, the crypto economy is leading to the development of an alternative financial and technological infrastructure. Read more about how cryptocurrency is one of the most well-known use cases for blockchain, apart from financial inclusion and connectivity.

DeFi, Smartphones and Crypto’s Growing Popularity

Mobile phones can be a gateway to financial inclusion, by facilitating payments and services of those unbanked. Learn more about alternative credit scoring for the DeFi age and how this generates faster payments for migrants.

The Future of Crypto Credit Access

Cryptocurrencies offer lenders clear opportunities to find new ways to leverage blockchain and other emerging technologies to aid unbanked populations. Find out more about crypto-lending for emergency credit and how DeFi could have even more radical implications for borrowing and lending.

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Can Crypto Lending Drive Financial Inclusion?

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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.