Community Detection for Mobile Money Fraud Detection

Safa El Ayeb (1, 2) , Baptiste Hemery (2) , Fabrice Jeanne (2) , Estelle Pawlowski Cherrier (1)

1 Equipe SAFE - Laboratoire GREYC - UMR6072
2 Orange Labs [Caen]

This paper presents an overview of a first-year PhD thesis. The main idea can be expressed as a community detection issue in a real network containing data from a telecommunications operator. More precisely, mobile money transactions will be studied, with a view to detecting fraudulent ones. For this purpose, we propose to address the problem by studying different community detection approaches. Indeed, in a network, communities are constituted between users sharing common habits, therefore fraudulent transactions may appear as unusual behaviors. The originality of this work is to process real data, gathered by a telecom operator. Complex network organization is expected, making this thesis quite promising.

Keywords: Mobile Money; Social Network Analysis; Community detection; Fraud detection; Mobile Money Social Network Analysis Community detection Fraud detection

Domains: Physics [physics]; Physics [physics]; Data Analysis, Statistics and Probability [physics.data-an]; Computer Science [cs]; Social and Information Networks [cs.SI]

Safa El Ayeb, Baptiste Hemery, Fabrice Jeanne, Estelle Pawlowski Cherrier. Community Detection for Mobile Money Fraud Detection. 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), Dec 2020, Paris, France. ⟨10.1109/SNAMS52053.2020.9336578⟩. ⟨hal-03949051⟩