Amid industry hype and patchwork global approaches, Standard Chartered is grounding its artificial intelligence (AI) ambitions in measurable outcomes, disciplined execution and a robust understanding of the technology’s risks. The bank’s results in the first half of 2025 provide a strong foundation, with an operating income of $10.9 billion, up 9% year-on-year (YoY), and profit before tax of $4.7 billion, up 22%. Wealth solutions income rose 24%, global banking 14% and global markets 28% YoY, while the cost-to-income ratio improved by 230 basis points to 54.7%, lifting return on tangible equity (RoTE) to 18.1% — well above the group’s medium-term target. These gains were driven by growth in affluent, cross-border and sustainability-linked banking, and reinforced by technology-led productivity improvements that are reshaping efficiency and returns across the franchise. David R. Hardoon, Standard Chartered’s global head of AI enablement since April, noted that AI is being applied “end-to-end, from corporate and investment banking, wealth and retail banking, all the way to technology and operations” across the organisation. Each use case, he emphasises, should create measurable impact, whether by generating new revenue, improving efficiency or mitigating risk. Governance as a foundation amid fragmented regulation Central to the Standard Chartered's AI approach is governance. Hardoon prefers the term “AI safety” over “responsible AI,” arguing that all AI should by definition be responsible. At the bank, AI safety is treated as a transversal discipline spanning data governance, cybersecurity, legal oversight, model risk and compliance. The framework builds on the fairness, ethics, accountability and transparency (FEAT) principles, which Hardoon was instrumental in pioneering during his tenure at the Monetary Authority of Singapore (MAS) in 2018, but extends them into a global methodology designed to make AI adoption systematic, repeatable and sustainable. This philosophy is critical as regulatory approaches diverge across regions. In the EU, the AI Act — finalised in 2024 — establishes a risk-based regime, imposing strict rules on high-risk financial applications, such as those used in credit scoring, to ensure data quality, transparency and bias mitigation. The US, by contrast, has no comprehensive AI law, but supervisors extend existing model risk rules (notably SR 11-7) to AI, emphasising validation and explainability, with some states moving to address algorithmic bias. In China, AI oversight combines the Cyberspace Administration’s algorithm filing and content labelling rules with the overarching Personal Information Protection Law (PIPL) and Data Security Law (DSL). Financial regulators, including the People’s Bank of China (PBoC), reinforce requirements for transparency, documentation and data localisation in financial AI deployments. Scaling adoption in culture, platforms and partnerships Governance alone, however, is not sufficient to effectively scale AI. Hardoon stresses the role of cultural adoption across Standard Chartered's diverse workforce to responsibly leverage the technology’s benefits to deepen client relationships and meet their cross-border banking needs. AI is, in his words, “about knowledge, a transversal across an organisation,” demanding new ways of asking questions, designing processes, and engaging and empowering clients worldwide. Training is designed not only to build technical fluency but to reframe how staff think about their roles. A flagship initiative is SC GPT, a generative AI tool rolled out in March, used by around 80,000 employees in 54 markets, making it one of the largest enterprise deployments of its kind in banking. Employees use it for document checks, content generation and knowledge retrieval. In one example, know your customer (KYC) and customer due diligence (CDD) officers developed macros through SC GPT that cut an eight-hour compliance review down to a single hour — an illustration of how frontline staff can adapt AI tools creatively within risk boundaries. Externally, the FX Intelligent Expert, built in collaboration with London Stock Exchange LSEG, delivers real-time video insights for wealth clients and is helping relationship managers personalise their engagement. First launched for retail customers in mainland China in December, the tool is now being extended across the Asian region. Meanwhile, advisory platforms such as MyWealth are being enhanced to better serve affluent clients with cross-border demands. Partnerships also play a crucial role in realising the bank's technology goals. Hardoon describes the model as “build, buy, blend” — some solutions are built in-house to strengthen institutional knowledge, while others are developed with partners. The collaboration with Alibaba Cloud announced in July, for example, focuses on customer service and risk management applications, alongside workforce upskilling. What sets Standard Chartered apart, Hardoon argues, is its insistence on building AI as enterprise platforms rather than isolated pilots. Reusability is central, and components are designed so different business units can scale solutions without rebuilding from scratch. This ensures that AI adoption is not episodic but cumulative, creating long-term efficiency. The bank also keeps capacity for experimentation through sandboxes, balancing the drive to productionise with the need to test emerging tools. Embedding trust while sustaining growth The bank’s incremental adoption now defines its competitive positioning, with aspirations to embed AI across the entire value chain — from compliance to advisory. Hardoon draws on his earlier experience at MAS, where he promoted governance not as a defensive exercise but as a competitive advantage. “To me, AI at the end of the day is a dialogue,” he explained — a dialogue with regulators, with employees, and with clients. Standard Chartered’s financial guidance underlines the stakes. Income is expected to grow at a 5–7% compound annual rate through 2026, with RoTE approaching 13%. Achieving these goals will hinge on disciplined cost management and the technology-led productivity gains flagged in its outlook — a space where AI is playing an expanding role. For Hardoon, the aspiration is for AI to become as seamless and taken-for-granted as a mobile phone or search engine: invisible to the user, but indispensable to performance. In an industry racing to experiment, Standard Chartered is prioritising the compounding effect of disciplined, enterprise-wide AI adoption. By embedding governance, scalability and trust at its core, the bank is positioning itself not just to prove AI’s utility, but to sustain its advantage as the technology becomes integral to global banking.
