The complete guide to Alternative Credit Scoring

In this eBook, we'll help you understand what alternative data is and, more specifically, how certain tools such as alternative credit scoring, can help your business grow by tapping new unexplored market segments.

Download this eBook to learn:

• What is alternative data
• Alternative credit scoring vs traditional credit scoring
• How alternative data can open up financial opportunities for credit invisibles
• How alternative credit scoring is being used in many industries


Read the white paper

What's inside?

Alternative credit scoring vs traditional credit scoring

The rise of alternative scoring with alternative data has completed and improved the ability to analyse and score the financial history of a person. Find out the differences between alternative and traditional credit scoring, and how they work together to create the perfect optimisation of results.

Alternative data opening up financial opportunities for credit invisibles

Over 2 billion people globally are without formal financial services access because their data is not held on traditional sources. Learn more about how alternative data has enhanced the credit review process to significantly increase credit approval rates and new client portfolios, helping credit invisibles.

Alternative credit scoring is used in many industries

The utility of alternative data is so broad that it reaches multiple industries including Bank & Consumer Finance, Buy Now Pay Later, Digital Lending, Neobanks & Challenger Banks and Ride-Hailing. Understand in more detail how these industries succeed with alternative credit scoring.

Please Enter Business Email Address
Thank you! You can download the whitepaper/ebook below. We'll also email you a copy for safe keeping.
Read the white paper
Oops! Something went wrong while submitting the form.
Please Enter Business Email Address
Thank you! We'll be sending your whitepaper/ebook shortly. Keep an eye on your inbox.
Oops! Something went wrong while submitting the form.
Read the white paper

The complete guide to Alternative Credit Scoring

Please fill in your details to get exclusive access to the e-book

Thank you! Your submission has been received and we will send you your copy shortly.

Oops! Something went wrong while submitting the form.

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.