Machine Learning Across Cultures: Modeling the Adoption of Financial Services for the Poor
This work addresses the challenge of effectively targeting financial services for the poor, but it is incremental as it highlights context-dependence without proposing a new solution.
The study tackled the problem of identifying individuals most likely to adopt mobile money services in developing economies, using data from Ghana, Pakistan, and Zambia, and found that predictive models fail to generalize across different cultural contexts.
Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit from access to the new technology. Here, we combine terabyte-scale data from three different mobile phone operators from Ghana, Pakistan, and Zambia, to better understand the behavioral determinants of mobile money adoption. Our supervised learning models provide insight into the best predictors of adoption in three very distinct cultures. We find that models fit on one population fail to generalize to another, and in general are highly context-dependent. These findings highlight the need for a nuanced approach to understanding the role and potential of financial services for the poor.