Generative AI is transforming retail banking in Asia-Pacific by integrating advanced AI capabilities with data management and core banking systems. In doing so, banks stand to improve customer engagement, operational efficiency, risk management, and regulatory compliance. Generative AI has the potential to significantly shape the future of retail banking, underpinned by ongoing investment in innovation and technology.
Current state of generative AI adoption
Recent advancements in generative AI, including cutting-edge language models like GPT-4, are enabling financial institutions to harness AI’s capabilities in novel ways. Synthetic data generation — the creation of data using algorithms rather than real-world collection — has emerged as a key enabler, providing scalable, automated solutions for diverse banking needs.
However, the IDC Data and AI Pulse: 2024 study, commissioned by SAS, highlights uneven AI adoption across Southeast Asia. The same study also reports that only 23% of organisations in the region are considered transformative in their AI use, characterised by long-term investment strategies that reshape markets and enhance customer experiences.
Among the leaders, Singapore stands out with robust AI adoption, while Malaysia and Thailand are positioning themselves as emerging markets, gradually embracing AI’s potential to improve efficiency and profitability. However, these countries frequently take a cautious wait-and-see approach, closely monitoring technological advancement before implementing comprehensive AI policies.
Challenges hindering AI implementation
Despite its potential, generative AI faces major difficulties. According to IDC, 40% of respondents cited poor data quality as a reason AI initiatives fail, followed by privacy concerns (38%) and limited data access (36%). Early adopters also contend with a lack of skilled personnel (41%), high implementation costs (30%), and unclear evaluation criteria for AI solutions (29%).
Singapore’s financial institutions, particularly those under the guidance of the Monetary Authority of Singapore (MAS), are tackling these issues head-on. The MAS-led Project MindForge consortium has developed a comprehensive generative AI risk framework that includes seven critical risk dimensions: fairness and bias, ethics and impact, accountability and governance, transparency and explainability, legal and regulatory compliance, monitoring and stability, and cybersecurity. Additional risks, such as hallucinations in AI outputs, prompt injection attacks, and data leakage, exacerbate adoption.
Applications of generative AI in retail banking
Generative AI offers transformative opportunities in retail banking, including:
- Risk Identification: Improving predictive analytics to mitigate financial risks.
- Customer engagement: Delivering hyper-personalised communication and AI-driven marketing.
- Operational efficiency: Automating repetitive tasks and streamlining workflows.
- Knowledge management: Simplifying market research and enhancing sales efficiency.
While these applications address evolving customer demands and strengthen competitive agility, overcoming trust and governance challenges is essential for maximising AI’s potential.
Ethical and regulatory considerations
Trustworthy AI is crucial for sustainable adoption. Issues related to bias, fairness, and regulatory compliance continue to dominate industry discussions. Leaders must handle more complex difficulties like intellectual property concerns, data privacy, and accountability frameworks. According to IDC, 80% of organisational leaders express concerns about data privacy and security, while only 10% feel prepared to meet regulatory requirements.
Emerging solutions, such as data anonymisation, encryption, and privacy-preserving machine learning techniques, assist organisations in striking a balance between innovation and compliance. Transparency, explainability, and human oversight are essential for ethical AI decision-making. These measures build trust and help organisations detect biases and improve fairness in AI-driven outcomes.
Emerging trends and societal impacts
The integration of generative AI accelerates the current digital transformation in banking. Banks are investing in advanced digital platforms, mobile apps, and digital-only services to enhance accessibility and convenience. The use of complementary technologies such as augmented reality, virtual reality, and the internet of things is growing as well, resulting in a more personalised and immersive customer experience.
Economically, generative AI is influencing industries and job markets, opening up new potential for innovation and growth. It is changing the way individuals and businesses interact with technology, causing changes in creativity, communication, and human-AI collaboration.
Path forward for organisations
To unlock generative AI’s full potential, organisations must establish robust data governance frameworks. According to IDC, 32% of local organisations cite data governance and privacy concerns as key barriers to AI advancement. Clear policies and processes are essential for ensuring responsible and transparent AI systems.
Explainable AI — technology that allows users to understand the logic behind AI decisions — is vital for addressing biases and ensuring equitable outcomes. Human intervention remains a key safety measure, allowing for manual overrides in complex or ethically difficult situations. These practices ensure that AI systems meet the different demands of users while minimising unintended consequences.
While generative AI tools have great potential, they must be underpinned by a strong data foundation. Contaminated data can skew AI outcomes, underscoring the importance of clean and reliable data preparation. Enterprises that prioritise data integrity and adopt a structured approach to AI implementation will be better positioned to achieve transformative results.
Paving the way for innovation in banking
Generative AI is advancing retail banking in the region to unprecedented levels of innovation and efficiency. However, long-term success necessitates a balanced approach that goes beyond short-term operational gains. Financial institutions can drive meaningful societal impact by concentrating on transformative outcomes such as enhanced customer experiences and innovative market solutions.
