 Fraud and anomaly detection models in banks: a systematic analysis and literature connectionAlex Cerqueira Pinto, Mathias Tessmann and Alexandre Vasconcelos Lima International Journal of Bibliometrics in Business and Management, 2024, vol. 3, issue 2, 182-205 Abstract: This paper seeks to analyse and verify existing connections in the literature on fraud detection in banks. For this, 227 papers published until September 2022 in the Web of Knowledge through the PRISMA protocol are analysed and classified. The works were identified using the keywords 'fraud', 'model', 'detection', 'banking' and 'risk' and classified into 12 categories, such as type of study, approach, cut, design, nature, the purpose of study, method, spatial scope, period of study, focus, data used and results. Based on the classification, statistics of complex networks are also used to identify the existing citation connections between them. The results show that there is a dissemination of the use of machine learning techniques together with business rules to detect possible cases of fraud and a growing increase in cases of fraud with social engineering. These findings are useful for the scientific literature that investigates operational risk professionals of banks. Keywords: fraud detection; anomaly detection; systematic literature review; bibliometric; machine learning; banks. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbbmi:v:3:y:2024:i:2:p:182-205 Access Statistics for this article More articles in International Journal of Bibliometrics in Business and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.