Financial technology forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior

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dc.authorscopusid57204804487
dc.authorscopusid38863437300
dc.authorscopusid57193792516
dc.authorscopusid24481107300
dc.contributor.authorAl-Khowarizmi
dc.contributor.authorWatts, Michael J.
dc.contributor.authorEfendi, Syahril
dc.contributor.authorKamil, Anton Abdulbasah
dc.date.accessioned2024-09-11T19:58:09Z
dc.date.available2024-09-11T19:58:09Z
dc.date.issued2024
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractFinancial technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. FinTech can make any transactions anywhere with the pillars of peer-to-peer (P2P) lending, merchants, and crowdfunding. In the P2P lending pillar, there are borrowers and lenders who are digitized in FinTech devices. FinTech in Indonesia is controlled by a state agency called the financial services authority or otoritas jasa keuangan (OJK). In the movement of P2P lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as evolving connectionist system (ECoS), which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a mean absolute percentage error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1=0.6 and a learning rate 2=0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders. © 2024, Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.sponsorshipUniversitas Muhammadiyah Sumatera Utaraen_US
dc.description.sponsorshipOur thanks go to Prof. Dr. Agussani, M.AP. The rector of the Universitas Muhammadiyah Sumatera Utara who have supported this research process in terms of funding.en_US
dc.identifier.doi10.11591/ijai.v13.i2.pp2386-2394
dc.identifier.endpage2394en_US
dc.identifier.issn2089-4872en_US
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85190857867en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2386en_US
dc.identifier.urihttps://doi.org/10.11591/ijai.v13.i2.pp2386-2394
dc.identifier.urihttps://hdl.handle.net/11363/8434
dc.identifier.volume13en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofIAES International Journal of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectEvolving connectionist system; Financial technology; Forecasting; Peer-to-peer lendingen_US
dc.titleFinancial technology forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavioren_US
dc.typeArticleen_US

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