Improving Credit Decisions and Lifting Financial Inclusion in Southeast Asia

It is clear that the conventional ways of assessing creditworthiness will not hold strong in 2020 and beyond. With COVID-19 and payment holidays disrupting the way credit application and assessment is traditionally done, and with new, unconventional customer segments – gig economy workers, Gen Z population, small business owners – requiring credit to keep making a living, relying on alternative data and digital channels is not an option anymore.

Bringing millions of new consumers into the mainstream financial system is by no means easy. It involves careful and innovative solutions to manage risks around potential identity fraud, to conduct secure customer authentications, and to lift consumer confidence in personal data security. In this whitepaper we outline how digitisation in financial services, along with innovative new approaches to credit scoring for the underbanked, may lead to better outcomes for millions and earn early movers loyalty from an emerging consumer class.

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What's inside?

Role of Digital Credit Scores in All Areas of Banking

From loan originations to customer experiences and activation - explore how digital scorecards make everything better.

Increased Revenue Opportunities from Advanced Credit Decisions

Gamified user experience and intuitive UX and UI hooks customers for life - find out more inside.

Case Studies of Transforming Financial Services with AI

Read about the first movers from across Southeast Asia who made digitization - front end and backend - their goal.

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Improving Credit Decisions and Lifting Financial Inclusion in Southeast Asia

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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.