Score! Non-Bank Lending and the Future of Fair Credit

Getting fair credit to people who need it is a global problem. The pandemic has made this a harder problem to address because there are now even greater pressures to balance credit access with credit risk management.

Traditional banks and lenders often have limited abilities to innovate fast and radically transform how they serve customers. On the other hand there are the customer-obsessed, tech-driven ecosystems that would love to integrate financial solutions anywhere they can. However, unlike the older, more battle-scarred banks, these tech titans have no experience handling crises of this scale and little preparation for juggling over-regulation and compliance, banking processes.

Each player has a role to play in shaping the future of credit - but will this be for the good of consumers? For the full insights into the future of fair credit, download our ebook here.


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What's inside?

Why are more people borrowing from non-banks?

Learn about the fundamental reason why consumers are changing their approach to borrowing, to financial services.

Introduction to Embedded Scoring

Enter the world of embedded scoring and get an insight into how companies are already using it for embedded lending.

Will fair credit be in reach by 2030?

What does this great unbundling of financial services and the arrival of embedded scoring mean for the future of lending and credit? Find out here.

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Score! Non-Bank Lending and the Future of Fair Credit

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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.