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<p>This study pursues dual objectives; firstly, to scrutinize the determinants critical to AI’s sustained utilization within banking and secondly, to scrutinize the intermediary role of technological knowledge amidst the factors of technology adaptation and continued usage intention. A survey engaging bank professionals who routinely employ AI for risk and fraud assessment was conducted. The data was analyzed using SmartPLS. 4 in two stages using structural equation modeling (SEM) and artificial neural network (ANN). The study proposes a hierarchical model showing that the perceived ease of use has a significant positive influence on the attitude toward the use of technology, but holds no direct significance on continued usage intention for artificial intelligence. The results are further validated using artificial neural network analysis. In light of these insights, bank policy strategists are better equipped to tailor approaches to navigate the structural and regulatory impediments to the AI adoption process.</p>

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Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月24日

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  • Komal Goyal,
  • Megha Garg,
  • Shruti Malik

摘要

This study pursues dual objectives; firstly, to scrutinize the determinants critical to AI’s sustained utilization within banking and secondly, to scrutinize the intermediary role of technological knowledge amidst the factors of technology adaptation and continued usage intention. A survey engaging bank professionals who routinely employ AI for risk and fraud assessment was conducted. The data was analyzed using SmartPLS. 4 in two stages using structural equation modeling (SEM) and artificial neural network (ANN). The study proposes a hierarchical model showing that the perceived ease of use has a significant positive influence on the attitude toward the use of technology, but holds no direct significance on continued usage intention for artificial intelligence. The results are further validated using artificial neural network analysis. In light of these insights, bank policy strategists are better equipped to tailor approaches to navigate the structural and regulatory impediments to the AI adoption process.