Relational Graph Neural Networks for Fraud Detection in a Super-App
environment
- URL: http://arxiv.org/abs/2107.13673v2
- Date: Fri, 30 Jul 2021 14:09:20 GMT
- Title: Relational Graph Neural Networks for Fraud Detection in a Super-App
environment
- Authors: Jaime D. Acevedo-Viloria, Luisa Roa, Soji Adeshina, Cesar Charalla
Olazo, Andr\'es Rodr\'iguez-Rey, Jose Alberto Ramos, Alejandro Correa-Bahnsen
- Abstract summary: We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App.
We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users.
Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
- Score: 53.561797148529664
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Large digital platforms create environments where different types of user
interactions are captured, these relationships offer a novel source of
information for fraud detection problems. In this paper we propose a framework
of relational graph convolutional networks methods for fraudulent behaviour
prevention in the financial services of a Super-App. To this end, we apply the
framework on different heterogeneous graphs of users, devices, and credit
cards; and finally use an interpretability algorithm for graph neural networks
to determine the most important relations to the classification task of the
users. Our results show that there is an added value when considering models
that take advantage of the alternative data of the Super-App and the
interactions found in their high connectivity, further proofing how they can
leverage that into better decisions and fraud detection strategies.
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