Machine learning applications for Fraud Detection in finance sector

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dc.authorscopusid57705454800
dc.authorscopusid57063298500
dc.contributor.authorTaşer, Pelin Yıldırım
dc.contributor.authorBozyiğit, Fatma
dc.date.accessioned2023-03-22T19:47:44Z
dc.date.available2023-03-22T19:47:44Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractDue to advances in information technology, instantaneous accessibility to financial services through digital channels has increased. Although digital platforms’ usage makes an individual’s life more comfortable, it may also cause some critical consequences like financial fraud which causes critical losses for companies in the industrial sector, investors, and governments. Identification of frauds can be challenging task for a human because it may be necessary to analyse high volume data during long time periods. An alternative is to use financial data as a fraud detection tool to automatically classify fraudulent activities. Currently, there are many practical solutions for automatically detect frauds in the finance domain. In this chapter, we examined on three different fraud types (bank fraud, insurance fraud, and corporate fraud) in finance sector and reviewed the studies using machine learning methods to detect financial fraud in a detailed manner. The findings from this review show that most commonly applied algorithms for financial fraud detection are Decision Tree, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest and most of machine learning-based studies were performed in bank fraud field. This chapter also reveals that deep learning and ensemble-based machine learning applications has been frequently preferred in recent years to improve detection performance of the frauds in finance sector. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.identifier.doi10.1007/978-981-16-8997-0_7
dc.identifier.endpage146en_US
dc.identifier.issn25097873
dc.identifier.scopus2-s2.0-85130846675en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage121en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-16-8997-0_7
dc.identifier.urihttps://hdl.handle.net/20.500.14034/847
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.journalAccounting, Finance, Sustainability, Governance and Frauden_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBank frauden_US
dc.subjectDeep learningen_US
dc.subjectEnsemble learningen_US
dc.subjectFinancial frauden_US
dc.subjectFinancial statement frauden_US
dc.subjectInsurance frauden_US
dc.subjectMachine learningen_US
dc.subjectMass marketing frauden_US
dc.subjectSecurities frauden_US
dc.subjectSupervised learningen_US
dc.subjectUnsupervised learningen_US
dc.titleMachine learning applications for Fraud Detection in finance sectoren_US
dc.typeBook Chapteren_US

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