Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement

Lucas Potin
Rosa Figueiredo
Vincent Labatut
Christine Largeron

Résumé

In the context of public procurement, several indicators called red flags are used to estimate fraud risk. They are computed according to certain contract attributes and are therefore dependent on the proper filling of the contract and award notices. However, these attributes are very often missing in practice, which prohibits red flags computation. Traditional fraud detection approaches focus on tabular data only, considering each contract separately, and are therefore very sensitive to this issue. In this work, we adopt a graph-based method allowing leveraging relations between contracts, to compensate for the missing attributes. We propose PANG (Pattern-Based Anomaly Detection in Graphs), a general supervised framework relying on pattern extraction to detect anomalous graphs in a collection of attributed graphs. Notably, it is able to identify induced subgraphs, a type of pattern widely overlooked in the literature. When benchmarked on standard datasets, its predictive performance is on par with state-of-the-art methods, with the additional advantage of being explainable. These experiments also reveal that induced patterns are more discriminative on certain datasets. When applying PANG to public procurement data, the prediction is superior to other methods, and it identifies subgraph patterns that are characteristic of fraud-prone situations, thereby making it possible to better understand fraudulent behavior.
Fichier principal
Vignette du fichier
ECML_HAL.pdf (993.64 Ko) Télécharger le fichier
DIAPO_ECML_PKDD_LUCAS_POTIN.pdf (593.97 Ko) Télécharger le fichier
Poster_ECML_POTIN (1).pdf (2.11 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04131485 , version 1 (16-06-2023)

Identifiants

Citer

Lucas Potin, Rosa Figueiredo, Vincent Labatut, Christine Largeron. Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 (ECML PKDD), Sep 2023, Torino, Italy. ⟨10.1007/978-3-031-43427-3_5⟩. ⟨hal-04131485⟩
91 Consultations
103 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More