Discovering modern credit risk scoring in buy now, pay later

The idea of allowing consumers to pay for goods over a number of instalments is by no means a new invention. Layaway and instalment payments have been part of the financial and consumer landscape for decades. But now, after a slow process of modernisation, the payment method has been transformed, rebranded, and expanded to reach a much wider global demographic. And we call it Buy Now, Pay Later or BNPL.

While the pandemic has accelerated the uptake of BNPL, it has also raised valid questions about consumers’ true understanding of the products, their ability to afford what they are purchasing, and the potential impacts on their credit scores.

In this report, we will delve deeper into the buy now, pay later market, weigh up its enormous ongoing potential, and provide you with the latest thinking and research on how the sector can continue to thrive, by taking a leadership role in improving consumers’ credit.

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What's inside?

BNPL for Millenials, Gig-workers and More

Learn why BNPL is the most preferred mode of payment today and what you may be getting wrong about it.

Weighing the Risks Against the Profits

Find out what are the pitfalls of a poorly executed BNPL solution and how to get it get right in the first try.

Promise of Better Credit Decisions

Explore what technology can do to help you navigate your way to higher profits are reduced risks.

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Discovering modern credit risk scoring in buy now, pay later

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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.