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

Journal of Risk Model Validation

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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