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<p>The rapid integration of artificial intelligence (AI) has transformed Malaysia’s financial services, making youth the primary market for AI-enabled FinTech applications. However, high technological exposure does not guarantee adequate digital financial literacy (DFL). To address this gap, this study investigates the drivers of AI-enabled FinTech adoption among Malaysian youth by extending the UTAUT2 model to include DFL as a mediating variable. Utilizing a cross-sectional quantitative design, data were collected from 259 young adults in the Klang Valley and analyzed using partial least squares structural equation modeling (PLS-SEM). The results demonstrate that performance expectancy, hedonic motivation, price value, and habit significantly predict the behavioral intention to use these applications, whereas social influence, effort expectancy, and facilitating conditions are not significant drivers. Crucially, while behavioral intention strongly influences actual usage, this relationship is partially mediated by DFL. This highlights that technological intention alone is insufficient for effective adoption without corresponding digital financial competencies. By bridging the intention–behavior gap, this research provides vital empirical insights for policymakers, educators, and developers to design targeted financial literacy initiatives and responsible AI tools.</p>

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Beyond intention: the critical role of digital financial literacy in the actual usage of AI-enabled fintech applications in Malaysia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Agha Jahanzeb,
  • Teoh Yee Xin,
  • Hyder Ali

摘要

The rapid integration of artificial intelligence (AI) has transformed Malaysia’s financial services, making youth the primary market for AI-enabled FinTech applications. However, high technological exposure does not guarantee adequate digital financial literacy (DFL). To address this gap, this study investigates the drivers of AI-enabled FinTech adoption among Malaysian youth by extending the UTAUT2 model to include DFL as a mediating variable. Utilizing a cross-sectional quantitative design, data were collected from 259 young adults in the Klang Valley and analyzed using partial least squares structural equation modeling (PLS-SEM). The results demonstrate that performance expectancy, hedonic motivation, price value, and habit significantly predict the behavioral intention to use these applications, whereas social influence, effort expectancy, and facilitating conditions are not significant drivers. Crucially, while behavioral intention strongly influences actual usage, this relationship is partially mediated by DFL. This highlights that technological intention alone is insufficient for effective adoption without corresponding digital financial competencies. By bridging the intention–behavior gap, this research provides vital empirical insights for policymakers, educators, and developers to design targeted financial literacy initiatives and responsible AI tools.