Applicability of ai-based technologies to the interest-free finance sector: “the benefit sharing model” as a new approach

dc.contributor.authorYumuşak, İbrahim Güran
dc.contributor.authorLökçe, Ahmet
dc.contributor.authorYumuşak, İbrahim Güran
dc.date.accessioned2023-03-17T12:13:12Z
dc.date.available2023-03-17T12:13:12Z
dc.date.issued2022
dc.departmentİşletme ve Yönetim Bilimleri Fakültesi
dc.departmentLisansüstü Eğitim Enstitüsü
dc.description16. ULUSLARARASI BİLGİ, EKONOMİ VE YÖNETİM KONGRESİ BİLDİRİLERİ -- ISBN 978-625-7820-42-4 -- Editör: İbrahim Güran Yumuşak -- Yayıncı: MİM Danışmanlık, Eğitim ve Yayıncılık -- Hazırlayan: Özgür Kökalan Hüseyin İçen Murat Işıker -- Yayın Yılı: 13 Kasım, 2022 -- Basım Yeri: İstanbul-Türkiye -- Erişim Adresi: www.beykon.org E posta: kongre@beykon.org
dc.description16th INTERNATIONAL CONFERENCE ON KNOWLEDGE, ECONOMY & MANAGEMENT PROCEEDINGS -- ISBN 978-625-7820-42-4 -- Edited by Ibrahim Guran Yumusak -- Published by MİM Danışmanlık, Eğitim ve Yayıncılık -- Prepared by Özgür Kökalan Hüseyin İçen Murat Işıker -- Publishing Year: 13 November 2022 -- Web adress: www.beykon.org E mail: kongre@beykon.org.
dc.descriptionBildiri, İstanbul S. Zaim Üniversitesi İslam Ekonomisi ve Finans Doktora Programı’nda Prof. Dr. İbrahim Güran Yumuşak danışmanlığında Ahmet Lökçe tarafından başarıyla savunulan aynı başlıklı doktora tezine dayalı olarak hazırlanmıştır.
dc.description.abstractThis article examines the theoretical background of an approach to interest-free financial transactions called The Benefit Sharing Model. This model is examined via an algorithm dataset in the context of interest-free finance. The innovative approach presented here provides advanced computation and analysis opportunities in interest-free financial transactions, designed using a machine learning algorithm. In this respect, the article evaluates structuring the machine learning algorithm with constant and variable data set groups. The study proves that artificial intelligence-based technologies can be used more effectively by the stakeholders in the interest-free finance sector. Ultimately, it demonstrates that strengthening the existing contract models in the interest-free finance sector and increasing the use of financial technologies will contribute to the development of the sector. The Benefit Sharing Model proposed in this article, uses a machine learning algorithm that is designed to produce decision outputs in interest-free financial transactions. This approach is presented in order to realize financial transactions in accordance with the basic principles of the interest-free finance sector. In fact, in a financial engineering process designed on an interest-free basis, the most basic premise is the obligation to comply with certain principles. For this reason, more detailed transaction analyses are needed for interest-free financial transactions than conventional financial transactions where interest is considered a legitimate instrument.
dc.identifier.endpage110
dc.identifier.orcidİbrahim Güran Yumuşak |0000-0003-1655-9872
dc.identifier.startpage102
dc.identifier.urihttps://hdl.handle.net/20.500.12436/4496
dc.institutionauthorYumuşak, İbrahim Güran
dc.institutionauthorLökçe, Ahmet
dc.language.isoen
dc.publisherMİM Danışmanlık, Eğitim ve Yayıncılık
dc.relation.ispartof16. Uluslararası Bilgi, Ekonomi: ve Yönetim Kongresi / 16th International Conference on Knowledge, Economy & Management
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial intelligence
dc.subjectArtificial learning
dc.subjectCloud computing
dc.subjectInterest-free finance sector
dc.subjectFinancial technologies
dc.subjectBenefit sharing
dc.subjectBlockchain
dc.subjectSmart contracts
dc.titleApplicability of ai-based technologies to the interest-free finance sector: “the benefit sharing model” as a new approach
dc.typeConference Object
dspace.entity.typePublication
relation.isAuthorOfPublicationb1af8bcf-e8c3-415a-9914-ad737af36bd7
relation.isAuthorOfPublication.latestForDiscoveryb1af8bcf-e8c3-415a-9914-ad737af36bd7

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