What you should know about Earned Wage Access

Millions of workers around the world live under constant financial stress. For workers in Brazil, the UK, India, or the Philippines, money worries remain top of mind every single day.

Yes, the problem is not new. But the new economy has made it more difficult to address. The pandemic, too, has forced millions around the world to make difficult choices about staying safe amid coronavirus outbreaks versus working to buy food and pay bills. Fair and timely pay for hourly workers has become a global challenge that political leaders and major companies have so far failed to tackle.

How much workers get paid is not the only problem. How often they are paid matters just as much.

Read this e-Book and take a closer look at a rising trend that may offer a solution: Earned Wage Access (EWA).

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What's inside?

Why EWA matters

Learn more about how Earned Wage Access provides a solution to workers’ cash flow worries.

Pros and Cons

Find out the difference between EWA and payday loans, and if EWA products are really fair for both employees and employers.

Making sure EWA is fair

How does the embedded financial services make it possible for digital lenders to provide EWA through affordable and fast APIs and integrations? Find out more here.

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What you should know about Earned Wage Access

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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.