INTEGRATING MULTI-CRITERIA METHODOLOGY WITH SYMBOLIC REGRESSION ON LOAN MODELLING IN BANKING SECTOR

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dc.authoridhttps://orcid.org/0000-0002-4740-0007
dc.authoridhttps://orcid.org/0000-0003-0384-6872
dc.authoridhttps://orcid.org/0000-0002-6564-6326
dc.contributor.authorBumin, Mete
dc.contributor.authorÖzçalıcı, Mehmet
dc.contributor.authorErtuğrul, Ayşegül
dc.date.accessioned2026-04-13T06:59:39Z
dc.date.available2026-04-13T06:59:39Z
dc.date.issued2026
dc.departmentFakülteler, Sanat ve Sosyal Bilimler Fakültesi, İşletme Bölümü
dc.description.abstractAbstract Forecasting loans accurately is essential for the banking sector as it underpins effective risk management, capital allocation, and portfolio optimization. This study aims to model loans in the Turkish banking sector by integrating symbolic regression with multi-criteria decision-making methodologies. Monthly data from January 2004 to September 2024, derived from banks’ financial statements, are utilized for the analysis. The optimal parameter configuration for symbolic regression is determined using the TODIM (an acronym in Portuguese for Interative Multi-criteria Decision Making) methodology. The forecasting performance of symbolic regression is evaluated against established models, including Autoregressive Integrated Moving Average (ARIMA), Gaussian Process Regression (GPR), Support Vector Machines (SVM), Neural Networks (NN), Regression Trees (RT), and Long Short-Term Memory (LSTM) network models. The proposed approach is applied across private, public, and foreign banks, as well as the overall banking sector. A significant finding of this study is the identification of a robust relationship between loans and two critical variables: assets and deposits. These results underscore the Corresponding author importance of strengthening deposit mobilization strategies and enhancing asset utilization to effectively grow banks’ loan portfolios. © (2026), (Faculty of Organizational Sciences, University of Belgrade). All right reserved.
dc.identifier.citationBumin, M., Özçalıcı, M., & Ertuğrul, A. (2026). Integrating multi-criteria methodology with symbolic regression on loan modelling in banking sector. Yugoslav Journal of Operations Research. https://doi.org/10.2298/YJOR250615041B
dc.identifier.doi10.2298/YJOR250615041B
dc.identifier.endpage34
dc.identifier.issn03540243
dc.identifier.scopus2-s2.0-105033092698
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/20.500.12941/398
dc.indekslendigikaynakScopus
dc.institutionauthorBumin, Mete
dc.institutionauthoridhttps://orcid.org/0000-0002-4740-0007
dc.language.isoen
dc.publisherFaculty of Organizational Sciences
dc.relation.ispartofYugoslav Journal of Operations Research
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLoan Modelling
dc.subjectSymbolic Regression
dc.subjectMulti-Criteria Methodology
dc.subjectBanking Sector
dc.titleINTEGRATING MULTI-CRITERIA METHODOLOGY WITH SYMBOLIC REGRESSION ON LOAN MODELLING IN BANKING SECTOR
dc.typeArticle

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