Using Mobile Money Data to expand Financial Inclusion

In emerging markets where a limited proportion of the population have access to banking services, the old paper-based credit rating system that uses banking and financial data as basis for determining financial fitness locks out millions of people out of the social economic gains of the financial system.

Availability of mobile money transactional data and social media data provide a viable alternative to perform a credit rating on populations without access to banking service, hence improving financial inclusion.

We develop applications that are powered by machine learning and AI algorithms that utilize the alternative data to understand customer financial fitness.

In order to classify consumers into paying and non-paying using mobile data, multiple classification algorithms can be utilized to create predictions and choose the optimum classification approach for credit scoring.

This alternative approach to credit scoring can expand access to financial services to millions of people who would have otherwise been considered not creditworthy.

Given the unprecedented growth of mobile money services across Africa compared to other regions, using this approach will be supplemental and possibly essential to the growth of the financial industry.

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