Challenge accepted: How challenger banks can grow profitably

From the middle part of the past decade, challenger banking took more and more accounts away from traditional retail banks. Until the pandemic. Now, in the 2020s, challengers are becoming challenged. With customer expectations changing, traditional banks catching up and cost of acquiring customers increasing, some challenger banks were experiencing weak customer and deposit growth rates.

So what should challengers do, as their businesses reach maturity at the same time their ideal customers’ lives are being radically transformed? Access this ebook to find out all that and more.

Read the white paper

What's inside?

Data-led personalization and Products

Find out how challenger banks can beat the competition by using alternative data to their benefit.

In-depth case study

Dive into the story of Philippines' latest challenger bank tonik - how they are staying ahead and what they are doing differently.

Future trends

Explore the trends that will rock the industry for both challenger and traditional banks alike.

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Challenge accepted: How challenger banks can grow profitably

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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.