  The thin-file borrowers are customers for whom a creditworthiness assessment
is uncertain due to their lack of credit history; many researchers have used
borrowers' relationships and interactions networks in the form of graphs as an
alternative data source to address this. Incorporating network data is
traditionally made by hand-crafted feature engineering, and lately, the graph
neural network has emerged as an alternative, but it still does not improve
over the traditional method's performance. Here we introduce a framework to
improve credit scoring models by blending several Graph Representation Learning
methods: feature engineering, graph embeddings, and graph neural networks. We
stacked their outputs to produce a single score in this approach. We validated
this framework using a unique multi-source dataset that characterizes the
relationships and credit history for the entire population of a Latin American
country, applying it to credit risk models, application, and behavior,
targeting both individuals and companies.
  Our results show that the graph representation learning methods should be
used as complements, and these should not be seen as self-sufficient methods as
is currently done. In terms of AUC and KS, we enhance the statistical
performance, outperforming traditional methods.
  In Corporate lending, where the gain is much higher, it confirms that
evaluating an unbanked company cannot solely consider its features. The
business ecosystem where these firms interact with their owners, suppliers,
customers, and other companies provides novel knowledge that enables financial
institutions to enhance their creditworthiness assessment.
  Our results let us know when and which group to use graph data and what
effects on performance to expect. They also show the enormous value of graph
data on the unbanked credit scoring problem, principally to help companies'
banking.
