Attention-based Dynamic Multilayer Graph Neural Networks for Loan
  Default Prediction
Sahab Zandi Kamesh Korangi María Óskarsdóttir Christophe Mues Cristián Bravo
Whereas traditional credit scoring tends to employ only individual borrower-
or loan-level predictors, it has been acknowledged for some time that
connections between borrowers may result in default risk propagating over a
network. In this paper, we present a model for credit risk assessment
leveraging a dynamic multilayer network built from a Graph Neural Network and a
Recurrent Neural Network, each layer reflecting a different source of network
connection. We test our methodology in a behavioural credit scoring context
using a dataset provided by U.S. mortgage financier Freddie Mac, in which
different types of connections arise from the geographical location of the
borrower and their choice of mortgage provider. The proposed model considers
both types of connections and the evolution of these connections over time. We
enhance the model by using a custom attention mechanism that weights the
different time snapshots according to their importance. After testing multiple
configurations, a model with GAT, LSTM, and the attention mechanism provides
the best results. Empirical results demonstrate that, when it comes to
predicting probability of default for the borrowers, our proposed model brings
both better results and novel insights for the analysis of the importance of
connections and timestamps, compared to traditional methods.