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Singapore’s finance firms pledge to train 80,000 local staff in AI skills

Singapore’s major financial institutions have pledged to train more than 80,000 local employees in AI skills as the country moves to cushion white-collar workers from potential job disruption. A pioneer batch of 23 banks, insurers and asset managers has committed to train all their Singapore staff by 2028, and more than half of them have already gone through programmes recognised by an industry…

Singapore’s finance firms pledge to train 80,000 local staff in AI skills

Singapore's leading financial institutions have pledged to train over 80,000 local workers in AI skills to mitigate potential job displacement caused by the technology. In a pioneering effort, 23 banks, insurers, and asset managers have committed to training all their Singapore-based staff by 2028, with more than half already completing recognized industry programs.

Deputy Prime Minister Gan Kim Yong highlighted the firms' intention to study job changes and experiment with training and job redesign strategies, initially focusing on leaders, wealth managers, and operations staff. This move aims to ensure that AI contributes to better careers for Singaporeans and enhances the workforce's productivity.

The initiative follows concerns that AI could render jobs obsolete faster than workers can adapt, especially in the finance sector, which constitutes about 14% of Singapore's economy and employs around 200,000 professionals. MAS managing director Chia Der Jiun emphasized the need to support workers as jobs and tasks evolve. This initiative builds upon earlier efforts by Singapore's three local banks to train all 35,000 employees in AI readiness.

Meanwhile, Singapore's Prime Minister Lawrence Wong joined global leaders in calling for control and international oversight of advanced AI models, warning of AI systems bypassing safeguards and exploiting vulnerabilities. Wong underscored the importance of maintaining human control over AI systems.

Written by urgent.news from South China Morning Post's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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