Self-organizing maps for fraud profiling in leasing

Abstract

Fraud is intended and planned activity aimed at achieving material or immaterial gains against interests of an organization or a person. It often occurs in financial industries, such as banking, insurance, and leasing. The goal of this paper is to present a novel approach to profiling fraudulent behavior in leasing companies, using self-organizing maps. Dataset of one leasing company that consists of both fraudulent and non-fraudulent transactions has been analyzed. Cluster analysis has been applied using the self-organizing maps algorithm, with the support of Viscovery SOMine software. Five clusters were identified, that have a different structure according to an industry of the client, previous experience with a client, type of a leasing object, and status of a leasing object (new or used). The clusters were compared using chi-square test according to proportion of fraudulent and non-fraudulent transactions, resulting in profiles of clients and leasing objects that are more prone to fraudulent behavior.