  In this paper we present the impact of alternative data that originates from
an app-based marketplace, in contrast to traditional bureau data, upon credit
scoring models. These alternative data sources have shown themselves to be
immensely powerful in predicting borrower behavior in segments traditionally
underserved by banks and financial institutions. Our results, validated across
two countries, show that these new sources of data are particularly useful for
predicting financial behavior in low-wealth and young individuals, who are also
the most likely to engage with alternative lenders. Furthermore, using the
TreeSHAP method for Stochastic Gradient Boosting interpretation, our results
also revealed interesting non-linear trends in the variables originating from
the app, which would not normally be available to traditional banks. Our
results represent an opportunity for technology companies to disrupt
traditional banking by correctly identifying alternative data sources and
handling this new information properly. At the same time alternative data must
be carefully validated to overcome regulatory hurdles across diverse
jurisdictions.
