Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1954
Title: Prediction of Loan Decisions With Optical Neuroimaging (fnirs) and Machine Learning
Authors: Girişken, Yener
Son Turan, Semen
Çakar, Tuna
Ertuğrul, Seyit
Sayar, Alperen
Keywords: optical brain imaging
Brain
machine learning
neurofinance
credit decision
Neuroscience
neuroeconomics
Near-Infrared Spectroscopy
fNIRS
decision making
Publisher: IEEE
Source: Çakar, T., Son, S., Sayar, A., Girişken, Y., & Ertuğrul, S. (2023, July). Prediction of Loan Decisions with Optical Neuroimaging (fNIRS) and Machine Learning. In 2023 31st Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
Abstract: The successful applications of neuroscientific methods and artificial learning approaches have increased in applied fields such as economics, marketing, and finance in the last decade. In this study, a prediction model was developed using the output of optical neuroimaging (fNIRS) measurements from the prefrontal brain regions while 40 participants made decisions for 35 credit offers. The aim was to predict participants' responses to credit offers using artificial learning methods based on four metrics obtained over time from the optical neuroimaging system. The findings of the study indicate that the first 6 seconds (prior to the response entry) are particularly critical. While the performance rate in the developed prediction models is found to be higher, especially in tree-based algorithms, this paper includes a performance comparison of 5 models specifically.
URI: https://hdl.handle.net/20.500.11779/1954
https://doi.org/10.1109/SIU59756.2023.10224010
ISBN: 979-8-3503-4355-7
ISSN: 2165-0608
Appears in Collections:İşletme Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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