Evidence map›Paper›PMID 38375365›Full record

ArticleFrontiers in human neuroscience2024

Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study.

Tuna Çakar, Semen Son-Turan, Yener Girişken, Alperen Sayar, Seyit Ertuğrul, Gözde Filiz, Esin Tuna

Abstract read
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Article in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Tuna ÇakarDepartment of Computer Engineering, MEF University, Istanbul, Türkiye.
Semen Son-TuranDepartment of Business Administration, MEF University, Maslak, Türkiye.
Yener GirişkenFaculty of Economics and Administrative Sciences, Final International University, Istanbul, Türkiye.
Alperen SayarInformatics Technologies Master Program, MEF University, Istanbul, Türkiye.
Seyit ErtuğrulInformatics Technologies Master Program, MEF University, Istanbul, Türkiye.
Gözde FilizComputer Science and Engineering Ph.D. Program, MEF University, Istanbul, Türkiye.
Esin TunaDepartment of Psychology, MEF University, Istanbul, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. Methods: The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Results: Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. Discussion: This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

Indexed as

consumer neurosciencecredit decision predictionfinancial decision-makingfunctional near-infrared spectroscopy (fNIRS)machine learning algorithmsneural activation patternsneurofinanceprefrontal cortex (PFC)

Identifiers

PMID38375365
PMCPMC10875049

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.