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

IAES International Journal of Artificial Intelligence · Vol. 13 No. 2 (2024) · hlm. 2386–2394

DOI
10.11591/ijai.v13.i2.pp2386-2394
ISSN
2252-8938, 2089-4872
Terbit
2024-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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40 penulis · 114 tautan kepenulisan bersama · 63 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Sumatera Utara
  • Politeknik Negeri Medan
  • Universitas Muhammadiyah Sumatera Utara
  • Media Design School
  • İstanbul Gelişim Üniversitesi
  • Telkom University
  • Universitas Bumigora
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Rekan penulisOrganisasiArtikel bersama
Arif Ridho LubisPoliteknik Negeri Medan6
Muhammad ZarlisUniversitas Sumatera Utara5
Muharman LubisTelkom University4
Romi Fadillah RahmatUniversitas Sumatera Utara4
Suherman SuhermanUniversitas Sumatera Utara3
Herman MawengkangUniversitas Sumatera Utara2
Herman MawengkangUniversitas Sumatera Utara2
Mahyuddin K. M. NasutionUniversitas Sumatera Utara2
Dadang PriyantoUniversitas Bumigora1
Mochamad WahyudiUniversitas Bina Sarana Informatika1
Hengki Tamando SihotangAfiliasi tidak diketahui1
Desi VinsensiaAfiliasi tidak diketahui1
Erna Budhiarti NababanUniversitas Sumatera Utara1
Heru MardiansyahUniversitas Sumatera Utara1
Saib SuwiloUniversitas Sumatera Utara1
Naemah MubarakahUniversitas Sumatera Utara1
Poltak SihombingUniversitas Sumatera Utara1
Fahmi FahmiUniversitas Sumatera Utara1
Julham JulhamPoliteknik Negeri Medan1
Idham KamilPoliteknik Negeri Medan1
Ferry FachrizalPoliteknik Negeri Medan1
Hariyadi HariyadiUniversitas Muhammadiyah Sumatera Barat1
Sumarlin SumarlinUniversitas Sumatera Utara1
Amrullah AmrullahUniversitas Muhammadiyah Sumatera Utara1
Akbar IdamanUniversitas Satya Terra Bhinneka1
Marischa ElvenyUniversitas Sumatera Utara1
Rahmad B. Y. SyahMedan Area University1
Mahyuddin K. M. NasutionUniversitas Sumatera Utara1
Sarah PurnamawatiUniversitas Sumatera Utara1
Mhd Faris PratamaUniversitas Sumatera Utara1
Sharfina FazaPoliteknik Negeri Medan1
Afritha AmeliaPoliteknik Negeri Medan1
Irvan IrvanUniversitas Muhammadiyah Sumatera Utara1
Habibi Ramdani SafitriPoliteknik Negeri Medan1
Michael J. WattsMedia Design School1
Akrim AkrimUniversitas Muhammadiyah Sumatera Utara1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Financial technology forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior</dc:title>
	<dc:creator>Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:creator>Watts, Michael J.</dc:creator>
	<dc:creator>Efendi, Syahril</dc:creator>
	<dc:creator>Abdulbasah Kamil, Anton</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Evolving connectionist system; Financial technology; Forecasting; Peer-to-peer lending</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23623</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i2.pp2386-2394</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 2386-2394</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23623/14041</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>