随着信息技术和智能化技术（如大数据、人工智能、云计算等）的迅速发展，银行业面临愈加复杂的市场竞争挑战。中国人民银行提出要坚持创新驱动，高质量推进金融数智化转型，构建现代金融体系，为实现共同富裕贡献力量。在此背景下，提升银行数智化水平和竞争力离不开员工能力的提升与业务绩效的优化。因此，构建系统、科学、灵活的培训体系成为银行数智化转型的关键任务。然而，传统国有银行的培训体系普遍存在培训目标不清晰、评估单一、资源配置不合理等问题，难以有效支撑数智化转型需求，影响培训效果和员工发展。

本文以HA分行的员工培训体系为研究对象，采用文献研究法、问卷调查法和层次分析法等多种方法展开系统性研究。首先，通过文献研究法，分析国内外银行培训体系的相关理论和实践，为本文的研究提供了理论支持和框架。其次，通过问卷调查法，获取了HA分行员工对于现有培训体系的需求和反馈数据，涵盖了培训需求、内容设计、实施形式及评估反馈等方面。最后，运用层次分析法，对不同培训需求和内容的重要性进行了量化评估，帮助识别出员工培训需求的关键因素和优先级。这一组合研究方法为分析培训体系中存在的问题提供了全面的视角和精准的数据支持。

研究结果显示，HA分行现有培训体系存在多个方面的不足。具体来说，培训需求分析方式单一，难以精确捕捉员工的实际需求；培训内容与员工的工作需求匹配度不足，未能有效提升员工的技能水平；培训形式上缺乏灵活性，难以适应不同员工的学习偏好和工作时间安排；，评估反馈机制较为单一，评估结果未能充分用于改进培训内容和策略。针对这些问题，本文提出了优化策略，包括建立数据驱动的需求分析工具，以更准确地识别员工培训需求；引入智能化技术构建个性化培训模式，使培训内容更加贴合员工个体需求；通过多维度的评估方法，对培训效果进行动态监测和反馈调整，实现培训资源的合理配置和持续优化。本研究为HA分行在数智化背景下推进员工培训体系的科学化和智能化转型提供了实践依据。

With the rapid development of information and intelligent technologies, such as big data, artificial intelligence, and cloud computing, the banking industry faces increasingly complex competitive challenges. The People’s Bank of China has emphasized the need to drive development through innovation, advance financial digital transformation with high quality, and build a modern financial system that aligns with the digital economy, contributing to the achievement of common prosperity. Against this background, enhancing the digitalization level and competitiveness of the banking sector relies on improving employee capabilities and business performance. Therefore, building a systematic, scientific, and flexible training system has become a critical task in the digital transformation of banks. However, traditional state-owned banks often face issues such as unclear training objectives, limited evaluation methods, and inefficient resource allocation, which hinder their ability to meet the evolving demands of digital transformation, affecting the effectiveness of training and employee development.

This paper focuses on the employee training system at HA Branch, utilizing literature research, questionnaire surveys, and the Analytic Hierarchy Process (AHP) to conduct a systematic study. First, literature research was conducted to analyze relevant theories and practices in banking training systems domestically and internationally, providing theoretical support and framework for this study. Secondly, through questionnaire surveys, data on HA Branch employees’ needs and feedback on the current training system were collected, covering training needs, content design, implementation forms, and evaluation feedback. Finally, AHP was applied to quantify the importance of different training needs and content, helping identify key factors and priorities in employee training needs. This combination of research methods provides a comprehensive perspective and precise data support for analyzing issues within the training system.

The results reveal several shortcomings in the existing training system at HA Branch. Specifically, the approach to training needs analysis is narrow, making it difficult to accurately capture employees’ actual needs; the training content does not adequately match employees’ job requirements, thus failing to effectively enhance skill levels; the training format lacks flexibility, hindering adaptation to employees’ varied learning preferences and work schedules; additionally, the feedback mechanism is limited, with evaluation results not fully leveraged to improve training content and strategies. To address these issues, this paper proposes optimization strategies, including establishing a data-driven needs analysis tool for more accurately identifying employee training needs, introducing intelligent technologies to build a personalized training model to better align content with individual needs, and implementing a multidimensional evaluation method for dynamic monitoring and feedback to optimize resource allocation continuously. This research provides practical insights for HA Branch’s scientific and intelligent transformation of its training system in a digital context.

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