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

Abstract

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 Peet-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 Otoritas Jasa Keuangan or Financial Services Authority (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 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.