In the era of technological and industrial transformation, the digital economy has emerged as a key driver of global growth, with advanced technologies like big data, cloud computing, and AI accelerating the digitization of corporate finance. As a data-centric function, finance’s strategic role in organizations is intensifying, making financial intelligence critical for adapting to this digital landscape and enhancing competitiveness. However, while intelligent finance boosts efficiency and decision-making, it also introduces risks related to information security, technology dependency, and system integration. Prior research highlights that implementation success depends on factors like technological adaptability, data quality, and organizational resistance, which can impede progress or cause failure. This study examines the full lifecycle of intelligent finance implementation, identifying key risks through case studies, field research, and quantitative analysis. It reveals that despite efficiency gains, firms face hurdles in strategic alignment, technology adoption, and staff capabilities. Six major risk dimensions—technical effectiveness, data security, process management, and organizational adaptation—were identified. A mixed-methods assessment found moderate to high overall risk, with strategic, technical, and personnel risks most pronounced. To mitigate these, the study recommends clarifying transformation goals, selecting appropriate technologies, enhancing staff training, and improving data governance. While limitations in data collection and literature may introduce subjectivity, cross-validation ensures reliable conclusions. Future research should focus on dynamic risk identification and objective evaluation methods to refine intelligent finance implementation.

Cheng Sheng. The Impact and Path of Financial Technology on Accounting and Financial Informatization under the Background of Technological Revolution. International Journal of Business Management and Economics and Trade (2024), Vol. 6, Issue 1: 52-61. https://doi.org/10.38007/IJBMET.2025.060106.

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