Predicting Mobile Financial Service Adoption with Machine Learning

Mobile money in Africa has rapidly evolved from its traditional role as a payment service to a gateway for millions on the continent to gain access to an ever-increasing array of financial products and services. For banks and other traditional financial service providers, future profitability will greatly depend on their ability to form partnerships with mobile carriers and accurately target subscribers on the network with financial service offerings that are relevant. There is a compelling business argument for effective customer targeting and cross-selling: for banks, digital channels with a high uptake boost low-cost deposit mobilization and increase lending capacity; for mobile carriers, digital financial product offerings that meet subscriber needs deepen engagement and increase retention. In this post, I will explore how machine learning can be used to classify individuals into one of four categories based on the types of financial services they are most likely to use. This is an example of multi-class classification where the task involves using an algorithm to induce a mapping function between a given set of input features and a categorical target variable that takes on more than two values.