  Globally, two billion people and more than half of the poorest adults do not
use formal financial services. Consequently, there is increased emphasis on
developing financial technology that can facilitate access to financial
products for the unbanked. In this regard, smartphone-based microlending has
emerged as a potential solution to enhance financial inclusion.
  We propose a methodology to improve the predictive performance of credit
scoring models used by these applications. Our approach is composed of several
steps, where we mostly focus on engineering appropriate features from the user
data. Thereby, we construct pseudo-social networks to identify similar people
and combine complex network analysis with representation learning. Subsequently
we build credit scoring models using advanced machine learning techniques with
the goal of obtaining the most accurate credit scores, while also taking into
consideration ethical and privacy regulations to avoid unfair discrimination. A
successful deployment of our proposed methodology could improve the performance
of microlending smartphone applications and help enhance financial wellbeing
worldwide.
