Buy Now, Pay Later (BNPL) firm Mocasa has tapped alternative credit scoring company FinScore to engineer bespoke credit scoring models that mitigate risk while its expanding market reach.

Now in its second year of business, Mocasa is doubling down on its commitment to help bridge the financial gap in the country.

Through its 0-interest credit payment service, the Philippines-based fintech platform supplies a “smart” and fitting credit line to its users that is adjusted based on their spending habits and repayment behavior.

Julien Chien, Chief Operating Officer of Mocasa said,

“How you behave with the money you have is what matters to us. Credit history checks are a safeguard for lenders as much as it’s a hindrance for the mass market. Delivering the happy medium is what fuels our hunger for innovation.”

With more than 7 years of big data experience, FinScore harnesses AI and telco data to identify creditworthy individuals among the unbanked in less than a second.

Its flagship Telco Data Credit Score draws its high predictive power from over 400 telecommunication variables – including voice usage, top-up patterns, call durations, SIM age, and location, and cutting-edge machine learning techniques like Gradient Boosting and Neural Networks.

Around 44% of adult Filipinos have zero credit history even though almost the entire population owns at least one mobile phone.

As credit history remains a major obstruction to financial inclusion, FinScore said that it hopes that more banks and financial institutions become emboldened to tap the telco data market.

Christo Georgiev, Country Head and Chief Operating Officer of FinScore said,

“Creating a causality between how an individual uses their mobile and how their repayment habits are formed is almost unheard of. But FinScore has established a logical, data-driven relationship that all risk teams stand to gain from.

 

Alternative data is the new frontier for financial inclusivity. We help banks, lenders, and other financial institutions activate their full potential through unbiased credit scoring systems derived purely from AI, machine learning, and Telco Data. It’s a way to fairly assess sectors once tagged as high-risk or invisible.”