A Framework for Occupational Fraud Detection by Social Network Analysis

Sanni Lookman (1) , Selmin Nurcan (1)

1 UP1 - Université Paris 1 Panthéon-Sorbonne

This paper explores issues related to occupational fraud detection. We observe over the past years, a broad use of network research across social and physical sciences including but not limited to social sharing and filtering, recommendation systems, marketing and customer intelligence, counter intelligence and law enforcement. However, the rate of social network analysis adoption in organizations by control professionals or even by academics for insider fraud detection purpose is still very low. This paper introduces the OFD – Occupational Fraud Detection framework, based on formal social network analysis and semantic reasoning principles by taking a design science research perspective .

Mots clés: Design science; ontology; data mining; fraud detection; social net-work analysis; internal control; governance; risk; compliance

Domaines: Informatique [cs]

Sanni Lookman, Selmin Nurcan. A Framework for Occupational Fraud Detection by Social Network Analysis. CAISE 2015 FORUM, Jun 2015, Stockholm, Sweden. , CEUR Vol-1367, CAiSE Forum 2015. ⟨hal-01217355⟩