Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

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) · pp. 2386–2394

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

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 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.

Keywords

Collaboration network of the authors

40 authors · 109 co-authorship links · 5 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Sumatera Utara
  • Universitas Bina Sarana Informatika
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad ZarlisUniversitas Sumatera Utara5
Arif Ridho LubisAffiliation unknown5
Muharman LubisAffiliation unknown4
Romi Fadillah RahmatAffiliation unknown3
Herman MawengkangUniversitas Sumatera Utara2
Romi Fadillah RahmatAffiliation unknown2
Arif Ridho LubisAffiliation unknown2
Mahyuddin Khairuddin Matyuso NasutionAffiliation unknown2
Herman MawengkangAffiliation unknown2
Dadang PriyantoUniversitas Sumatera Utara1
Mochamad WahyudiUniversitas Bina Sarana Informatika1
Hengki Tamando SihotangAffiliation unknown1
Desi VinsensiaAffiliation unknown1
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 JulhamAffiliation unknown1
Idham KamilAffiliation unknown1
Hariyadi HariyadiAffiliation unknown1
Ferry FachrizalAffiliation unknown1
Sumarlin SumarlinUniversitas Halu Oleo1
Suherman SuhermanAffiliation unknown1
Amrullah AmrullahAffiliation unknown1
Akbar IdamanAffiliation unknown1
Mahyuddin K. M. NasutionUniversitas Sumatera Utara1
Marischa ElvenyUniversitas Sumatera Utara1
Rahmad B. Y. SyahAffiliation unknown1
Suherman SuhermanAffiliation unknown1
Sarah PurnamawatiAffiliation unknown1
Mhd Faris PratamaAffiliation unknown1
Sharfina FazaAffiliation unknown1
Afritha AmeliaAffiliation unknown1
Suherman SuhermanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<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>