As banks increasingly adopt AI, they must also ensure adherence to a framework of responsible AI in banking. The experiences of leading financial institutions like DBS offer valuable lessons, from strong data governance to enhanced productivity through AI.

A 2024 study by McKinsey found that AI adoption has surged, with 72% of companies now using the technology - up from just 50% six years ago. Analysts expect this trend to continue as more financial institutions embrace generative AI (Gen AI).

Building a responsible AI strategy in this industry in particular, requires the thoughtful incorporation of ethics and governance. DBS first began experimenting with AI and machine learning in 2014, giving the bank an early head start and garnering valuable insights that can help boards and management teams in the financial sector.

DBS started its AI journey as part of a larger digital transformation effort. The bank's efforts began with experimental projects with tech partners like IBM and A*STAR, but over time, these evolved into structured institutional programmes that generate enterprise-wide value.

Fast forward to today, DBS' early lead has yielded valuable lessons in deploying AI in finance, and at scale. Responsible and ethical AI use underpins the bank's operations. The bank leverages AI to enhance customer experience and deepen engagement - for example, by delivering AI-powered, hyper-personalised financial advice. In DBS, AI also plays a critical role in detecting fraud and sharpening decision-making across the organisation.

AI is also used to support employees, offering tailored career and upskilling roadmaps.

As of May 2025, DBS has deployed over 1,500 AI models across 370 use cases. These use cases are projected to bring the bank economic value of SGD 1 billion by 2025.

Scaling AI in finance and beyond

To scale AI ethically and effectively, banks should focus on three critical pillars: platform, process and people

.

• Platform. Quality data is the lifeblood of a good AI strategy. A centralised data platform - serving as a single source of truth - ensures data governance, data security and data to be found easily. An AI protocol repository will enable models to be reused.

DBS' implementations of a data platform and protocol repository have reduced the time-to-market for AI initiatives from 15 months to just under three months.

• Process. Robust data and AI governance frameworks will help manage potential risks such as bias in decision-making and data privacy concerns.

DBS follows four guiding principles – purposeful, unsurprising, respectful, explainable (PURE) – in its model data governance framework. A senior-level committee oversees AI use cases to ensure legal compliance and ethical integrity.

• People. Dedicated data teams should drive AI adoption across the organisation. AI professionals in cross-functional squads can develop and implement use cases.

Beyond data specialists, banks should invest in AI and data literacy training for all employees. This ensures everyone has a baseline understanding of AI, which includes responsible data collection and AI use.

Opportunities for Gen AI in banking

Gen AI presents significant opportunities for banks and other financial institutions to further enhance customer service, improve operational efficiency and drive innovation.

Gen AI-powered tools like DBS’ CSO Assistant can help customer service officers respond to queries more effectively. They do so by transcribing conversations in real-time, retrieving relevant information instantly, and assisting with documentation. The benefits include reduced call handling times and improved accuracy.

Secure, in-house Gen AI tools can also help employees generate content and find information, streamlining workflows across departments. One example is DBS GPT, a Gen AI program similar to ChatGPT which the bank developed.

Beyond productivity gains, Gen AI can help banks strengthen risk management and regulatory compliance. Banks can use the technology to detect fraudulent transactions, assess credit risk, and monitor regulatory compliance more efficiently.

AI-driven insights also speed up product development, allowing banks to analyse unstructured data, such as market reports and customer feedback. This helps banks to refine their product offerings and anticipate evolving financial needs, improving customer experience and engagement. These capabilities provide a competitive edge in an increasingly digital and data-driven industry.

Responsible AI in banking and beyond

While Gen AI offers immense potential, responsible AI adoption is critical, particularly in banking. In the highly regulated financial industry, where sensitive data and AI decisions directly shape customers’ finances, transparency, fairness and accountability are crucial.

To get the most out of AI, banks (and other organisations) must ensure that their AI applications align with regulatory requirements, ethical standards and business objectives – establishing governance frameworks as necessary.

This includes implementing and using principles such as fairness, transparency, and accountability. Doing so will prevent bias in decision-making, ensure explainability in AI outputs, and maintain trust with customers and regulators.

Additionally, maintaining strong human oversight is essential. DBS, for instance, takes a co-pilot approach to using AI, ensuring there is always a human in the loop. While Gen AI can enhance efficiency, it should complement and not replace human judgment. This is critical in sensitive functions such as risk assessment and customer interactions.

Of course, none of this works without understanding how AI functions. That’s why AI literacy matters. Employees should be trained and empowered to confidently manage and interpret AI-driven recommendations. At the same time, banks must prioritise data privacy and security, ensuring that AI models are trained on high-quality, well-governed customer data while keeping customer information safe from breaches or misuse.

Active participation in industry-wide AI governance initiatives also plays a key role in responsible AI adoption. Working together, regulators, financial institutions and technology experts can shape best practices to ensure AI use balances innovation with risk management.

By integrating AI and Gen AI in a strategic and responsible way, banks can harness the technology's benefits, while maintaining the trust and stability fundamental to the financial services sector.

Applying responsible and ethical AI in finance isn’t just the right thing to do – it’s a competitive advantage.