Financial technology forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Advanced Engineering and Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Financial 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.
Açıklama
Anahtar Kelimeler
Evolving connectionist system; Financial technology; Forecasting; Peer-to-peer lending
Kaynak
IAES International Journal of Artificial Intelligence
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
13
Sayı
2
