The emergence of Generative AI (Gen AI) has become a major force in reshaping Singapore's Financial Services sector. Thanks to its enhanced ability to understand natural language and process large, unstructured datasets, Gen AI is emerging as a potential game-changer for businesses. The technology is enabling our financial institutions to shift from routine, task-oriented activities to more strategic, proactive approaches that allow them to meet the evolving expectations of their customers while remaining globally competitive.

Financial institutions are already leveraging AI to improve operations. From supercharging chatbots for more personalised customer service to analysing large transaction datasets to prevent fraud in risk management, the technology is reshaping how work is done. Furthermore, AI is helping to expedite time-consuming labour, enabling employees to focus on higher-value work.

Despite the rapid advancements, the technology powering AI tools and Generative AI is evolving faster, outpacing its adoption for commercial use. If businesses in Singapore want to successfully integrate AI into their core operations, upskilling and reskilling our workforce is essential.

To guide this transformation, the Impact of Generative AI (Gen AI) on Financial Services Sector Jobs Transformation Map (JTM) was published in October 2025 by the Monetary Authority of Singapore (MAS), the Institute of Banking and Finance (IBF), and Skills and Workforce Development Agency. Examining key use cases and possible adoption trends in the financial services sector, the JTM assesses the impact these trends could have on job roles and highlights the skills the workforce needs to prepare for these potential transformations.

The strategic adoption of Gen AI can offer a wealth of benefits to financial institutions, helping them remain competitive in a fast-changing global environment. A vanguard of financial institutions are already leading the way, with most initial applications focused on improving customer service, employee productivity, and software development. 

AI as a Strategic Priority

Leading financial institutions around the world are making AI a strategic priority, with over 90% communicating its importance, and more than 80% exploring its use cases. Advancements in computational power have significantly reduced the marginal costs of intelligence and creativity. With its ability to understand natural language and process large, unstructured datasets, Gen AI is becoming a game-changer for the Financial Services sector. While traditional machine learning models remain effective for numerical and optimisation tasks, Gen AI is opening up entirely new frontiers for productivity and innovation.

Most Value Expected to come from 4 Key Business Functions

• Sales & Marketing: Gen AI models can produce hyper-personalised content and tailored messages at scale. They can also identify and prioritise high-value customer leads. For example, WeBank successfully automated video content creation, reducing production costs by 90% and allowing marketing specialists to produce their own content without relying on professional designers.
• Customer Operations: This area is being transformed by Gen AI's ability to offer proactive, self-service support. The technology can partially automate interactions with customers using natural language. For instance, ING’s Gen AI chatbot improved customer satisfaction by offering immediate, tailored help to customers, helping them avoid long wait times.
• Engineering & Technology: Gen AI code assistants are helping to address technical debt and speed up software delivery. These tools can generate initial code drafts, correct errors, and even accelerate testing. OCBC's in-house coding assistant, Wingman, has automated routine coding activities, allowing developers to write software faster and with fewer errors.
• Risk Management: In this field, Gen AI is enabling a shift from task-oriented activities to more strategic risk prevention. It can automate report generation and conduct automated research and risk assessments. Income Insurance leveraged a Gen AI tool to help practitioners quickly digest and apply complex risk assessment guidelines, empowering them to focus on higher-value tasks like risk evaluation.

Adoption Growth

As financial institutions increasingly adopt Gen AI, the complexity and scale of this work may necessitate the creation of dedicated job roles. These may not necessarily entirely new positions, but rather Gen AI-specialised versions of existing jobs that require specific expertise.

Singapore's financial institutions can learn a lot from each other and from leading global firms. They can also draw valuable insights from the Gen AI adoption journeys of other industries, particularly the technology and retail sectors. Initiatives such as the industry working groups organised by the Association of Banks in Singapore (ABS) provide a platform for stakeholders to share experiences, shape ideas, and learn from one another's successes.

The adoption of Gen AI is expected to augment jobs in the finance industry, some to a larger extent than others. As Gen AI is adopted and integrated into workflows, how a job evolves depends on two primary factors – the extent to which Gen AI is able to automate particular tasks, and whether Gen AI’s outputs could be used directly by others (such as one’s supervisor, colleague or customer) instead of by the employee. This transformation will reshape jobs across the Financial Services sector, impacting roles from client-facing positions like relationship managers to operational functions in customer service and risk management.

Job Roles That Will Require You to Do More

The majority of job roles are expected to be augmented by Gen AI. This means that the work tasks associated with these job roles will largely remain the same, but individuals in these roles can "do more" of their existing tasks in the same amount of time by leveraging Gen AI tools. Proficiency in Gen AI is essential for these employees to effectively integrate AI into their daily work.

For example, a software engineer can use Gen AI tools to augment their work tasks. They can leverage the technology to accelerate their speed in capturing and analysing user and business requirements. A software engineer can also use Gen AI tools to generate code more quickly, leveraging reusable code snippets and real-time suggestions for code improvements. As a result, software engineers can now undertake more software development projects within a given time, which enables them to build more solutions and address business needs. This translates to their ability to "do more" of their existing work.

Job roles requiring you to do more:

As the finance industry continues its transformation, new career opportunities and specialised jobs in financial services may emerge in response to the growing adoption of artificial intelligence and machine learning. A new blend of skills and expertise to manage these changes may be needed, particularly in areas like governance, data management, and strategy.

These emerging roles are often specialised versions of existing positions, reflecting the new opportunities available in this evolving landscape.

• AI/Gen AI Strategy and Transformation Lead: This role drives organisational change by developing and executing a comprehensive AI/Gen AI strategy. They provide thought leadership and collaborate across stakeholders to ensure the seamless deployment of AI solutions into existing business workflows. They alsofoster strategic partnerships to enhance AI capabilities.
• AI/Gen AI Product Manager: As the "conductor" of AI/Gen AI solutions, this role orchestrates the entire lifecycle of AI models. Their primary focus is ensuring that the AI solutions align with business objectives and effectively address user needs. They lead initiatives to evaluate and enhance AI processes and oversee the development and validation of AI models from initial concept to deployment.
• AI/Gen AI Engineer: They are the architects behind the scenes, responsible for designing and building the AI/Gen AI models, algorithms, and systems. Their expertise includes prompt engineering, context engineering, and tailoring these systems to solve specific business challenges.
• AI/Gen AI Data Management Lead: This role is critical for establishing a robust and ethical data foundation for AI/ GenAI applications. They develop and implement data strategies, ensure secure and compliant data practices and manage the flow of information to ensure accuracy and efficiency for AI applications.
• AI/Gen AI Policy and Ethics Officer: As AI/Gen AI becomes more integrated into core operations, this role establishes and maintains governance frameworks to ensure its responsible use. Their primary focus is on transparency, accountability, and ethical considerations. They research potential risks surrounding AI products and work with engineers to ensure all solutions comply with enterprise policies and best practices.
• AI Trust and Model Risk Specialist: This role focuses on building and maintaining trust in AI systems, ensuring transparency and accountability. These specialists develop the tools and processes needed to make AI models transparent and explainable. They also monitor compliance with regulatory requirements and ethical guidelines, ensuring that the organisation's AI models are both effective and safe.

As Gen AI continues to transform the financial services landscape, finance professionals must develop a comprehensive set of skills to stay relevant. These key competencies are crucial for integrating Gen AI into financial practices, meeting regulatory requirements, and driving innovation. Mastering these skills will be key for finance professionals to thrive in this evolving sector.

• Prompt Design: Acquiring the skills for crafting effective prompts is fundamental for working with Gen AI. This involves carefully designing prompts to elicit the desired responses from AI models and shaping their outputs according to specific business needs. A strong command of prompt design allows professionals to leverage Gen AI as a powerful tool for productivity and creativity.

• Gen AI Principles and Applications: A strong grasp of the core concepts, existing frameworks, potential applications, and wider implications of AI models is vital for effective and responsible implementation. Understanding how Gen AI and machine learning work allows professionals to identify opportunities for the technology to solve business problems and deliver value.

• Ethical and Regulatory Expertise: Given the sensitive nature of data in finance, a strong focus on ethics and compliance is non-negotiable. Professionals require skills in Gen AI data governance, implementing ethical frameworks, and ensuring compliance with regulatory, legal, and risk management policies. Expertise in these areas is key for building and maintaining trust in AI systems.