Fraud Prevention in the Leasing Industry Using the Kohonen Self-Organising Maps

• Published in: Organizacija 2020-05, Vol.53 (2), p.128-145
• Main Authors: Bach, Mirjana Pejić, Vlahović, Nikola, Pivar, Jasmina
• Format: Article
• Language: English
• Subjects: Clients; Clustering; Crime prevention; Croatia; Data mining; Fraud; Fraud prevention; Leasing; Leasing companies; Methodology; Self organizing maps; self-organising maps; Tractors; Viscovery SOMine; Ward algorithm
• ISSN: 1581-1832; 1318-5454; 1581-1832
• DOI: 10.2478/orga-2020-0009

: Data mining techniques are intensely used in various industries for the purpose of fraud prevention and detection. Research that focuses on the leasing industry is scarce, although frauds in the field of leasing occur rather often. First, we identify clusters of business clients in one leasing company by using the method of self-organising maps based on leasing contract attributes. Second, we compare clusters based on the presence of fraudulent clients, in order to develop fraudsters’ profiles. For detecting characteristics of fraudulent clients, we use a client database containing leasing contract attributes of one Croatian leasing company. In order to develop profiles of fraudulent clients, we utilise a clustering procedure with the Kohonen Self-Organizing Maps supported by Viscovery SOMine software. Five clusters were identified and labelled according to the modal values of attributes describing the leasing object and the industry in which the client operates: (i) New cars / Trade; (ii) Used trucks or tugboats / Other services; (iii) New machinery / Construction; (iv) New motors / Trade; and (v) New machinery and tractors / Agriculture. : Self-organising maps have proved to be a useful methodology for developing profiles of fraudulent clients in leasing companies. Companies can use our results and make additional efforts in monitoring clients from the identified industries, buying specific leasing objects. In addition, companies can apply our methodology to their own databases, in order to develop fraudster profiles for their specific purposes, and implement fraud alert mechanisms in their client database.