AUTHOR: Chimedtsogzol Yondon, Oyundari Byambaa, Ariunaa Sodov, Ankhbayar Chuluunbaatar
PUBLISHED IN: Volume 6 Issue 1
KEYWORDS: Fintech Loan, Credit Risk, Non-Performing Loan, Non-Bank Financial Institution, Individual Behavior, Artificial Neural Network, Two-Stage Model
ABSTRACT
This study aims to examine the impact of individuals on fintech lending positions. While short-term, fast-decision online lending services are rapidly developing in the Mongolian fintech lending industry, the failure to consider consumer financial education, psychological impact, and consumption habits in loan evaluation is one of the main reasons for the emergence of non-performing loans.
The PLS-SEM model was used to analyze the effects of behavioral variables such as financial literacy, risk perception, materialism, and emotionality on individual indebtedness. The results showed that financial literacy had the strongest positive effect on indebtedness. Risk perception positively affects emotionality and materialism, indicating that individual behavior could be explained indirectly. The model explains 32% of the variance in individual indebtedness (R² = 0.32).
The study concludes that the integration of individual behavioral and psychological determinants into fintech lending-risk assessment frameworks, coupled with the development of targeted borrower financial-literacy interventions, has the potential to substantially mitigate default risk among fintech loan recipients. This study demonstrates that individual behavioral indicators significantly shape individual indebtedness; therefore, validating the foregoing results using an artificial neural network (ANN) approach was methodologically appropriate.
DOI: https://doi.org/10.65194/IJBI-2026-1003
