Feb. 2, 2024, 3:46 p.m. | Sahab Zandi Kamesh Korangi Mar\'ia \'Oskarsd\'ottir Christophe Mues Cristi\'an Bravo

cs.LG updates on arXiv.org arxiv.org

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 …

assessment attention credit credit risk cs.lg dynamic graph graph neural networks network networks neural networks paper prediction q-fin.gn risk risk assessment scoring

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