Journal of Risk Model Validation
ISSN:
1753-9587 (online)
Editor-in-chief: Steve Satchell
Green risk identification and risk measurement in fintech: a particle swarm optimization fuzzy analytic hierarchy process and sparrow search algorithm quantile regression neural network approach
Li Zeng and Wee-Yeap Lau
Need to know
- This study applies the PSO-FAHP in conjunction with the SSA-QRNN to perform a comprehensive identification and assessment of financial risks in fintech.
- The study develops a framework that incorporates green risk components to assess both conventional and environmental risks in fintech, covering six essential first-level risk categories and 21 sub-indicators.
- The findings indicate that green risk factors are significantly influential in fintech. The weight outcomes disclose a trend in which operational risk and credit risk occupy the dominant positions among all primary risk indicators.
- Both the PSO-FAHP model and the SSA-QRNN model exhibit strong adaptability, stability, and precision in the assessment of FinTech risks.
Abstract
The development of financial technology (fintech) has given rise to a series of derivative potential risk issues, particularly in the domains of risk identification and risk measurement. This study analyzes the fintech sector by employing the fuzzy analytic hierarchy process combined with particle swarm optimization, as well as a quantile regression neural network (QRNN) model modified by the sparrow search algorithm (SSA), to comprehensively identify and measure financial risks. The SSA-QRNN model demonstrates considerable adaptability, high stability and high precision in assessing fintech risks. Suggestions are presented from diverse perspectives, offering valuable insights for risk management and sustainable development in the fintech sector.
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