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Drawing on theories pertaining to knowledge sharing, ageing at work and human resource practices for ageing workers, this article explores knowledge sharing challenges arising from the interaction between an increasingly ageing workforce and younger employees.

Contextually, the authors focus on China, where the pace of demographic transformations offers a unique opportunity to investigate knowledge sharing practices in their socio-economic context. Empirically, the authors analyse knowledge sharing behaviours and practices of retail banking professionals in a Chinese big four bank.

The encouragement of knowledge sharing between younger and older workers should be incorporated into organisations' human resource strategies. The availability of development, maintenance, utilisation and accommodative human resource practices signals to older workers that they are valuable and are worth investing in.

The authors’ contribution to theory and practice is twofold: starting with the identification of perceived knowledge sharing challenges, the authors’ analysis offers important contextually grounded insights into what types of managerial practices are relevant in eliciting successful knowledge sharing within organisations faced with an ageing workforce.

Artificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics.

The study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques.

FinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models.

The study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes.