Generative AI is driving sweeping changes to customer expectations. Financial institutions need to face this existential challenge head on.
Generative AI is one of the most significant technological leaps since the internet, presenting opportunities for innovation at scale. The stakes couldn’t be higher: IDC estimates AI could contribute US$19.9 trillion to the global economy by 2030, enabling breakthroughs that were once considered decades away. Yet many financial institutions appear unprepared to fully harness its power. With so much at stake, banks should be eager to capture AI’s benefits. So, what’s holding them back?
Guardrails, flexibility, and talent for generative AI adoption
Businesses adopting generative AI often grapple with several key concerns, including data privacy, security, talent, costs, and complexity. Social considerations also weigh heavily on decision-makers, with fears that AI-generated content might introduce bias. This risk highlights the need for businesses not only to invest in AI, but also to plan and manage these technologies strategically.
Regulatory compliance is another challenge, especially in finance, where strict rules govern data usage and technology integration. For example, companies must ensure that their AI solutions comply with data protection laws such as Japan’s Act on the Protection of Personal Information (APPI) and Singapore’s Personal Data Protection Act (PDPA).
Financial institutions and governments across Asia can also learn from Singapore’s approach to AI governance. The country’s Model AI Governance Framework for Generative AI, launched in May 2024, helps organisations manage risks while fostering innovation. By nurturing international dialogue on AI issues, Singapore aims to create a trusted environment for the development and deployment of generative AI across industries.
Companies must also address talent deficits in AI skills as they confront their AI readiness. According to Kyndryl’s first People Readiness Report, 65% of leaders in banking, financial services, and insurance (BFSI) say their workforce is not ready to successfully leverage AI in the workplace.
Further, while generative AI application development has traditionally relied on roles such as data engineering, platform building, and back-end development, expertise in prompt engineering, evolving regulation, and responsible AI governance are also required for effective deployment. For banks to truly harness the power of generative AI, they’ll need to strike a balance between upskilling their workforce and attracting specialised talent.
Innovating for a better customer experience
In the financial sector, generative AI’s power to enhance fraud detection, risk management, and personalised customer experiences is clear. Its ability to rapidly analyse vast datasets, which is crucial for risk management and investment strategies, makes it increasingly valuable for banks across Asia.
However, in a world in which AI will be everywhere, financial institutions must critically evaluate their AI strategies and whether their IT infrastructure can support them.
Many businesses still rely on decades-old, interdependent technology systems. According to Kyndryl Bridge data, 44% of servers, storage, networks, and operating systems within businesses are approaching or at end of life. Maintaining these legacy estates is increasingly unsustainable as upgrade cycles shorten, and the need to modernise is becoming impossible to ignore.
While banks are unlikely to fully abandon legacy systems, many are shifting to hybrid IT set-ups to leverage cloud technology and improve the customer experience. This approach enables faster innovation and strengthens security and compliance, allowing financial institutions to meet regulatory requirements while modernising their infrastructure. They’re also using regional data centres to comply with new data regulations, handle higher transaction volumes, and reduce operational costs.
As they move forward, financial enterprises must treat payments modernisation not as an isolated upgrade, but as part of a broader digital transformation strategy. That means aligning payments capabilities with enterprise architecture, data governance, cybersecurity frameworks, and compliance protocols. It means investing not only in the right technologies, but also in the organisational agility to evolve with them.
Building trust and winning customers
What financial institutions do next to harness generative AI will determine their success. BCG reports that Asia-Pacific enterprises are moving beyond experimentation, with more than 80% of AI investments now focused on transforming core business functions and offerings. Financial institutions are no exception; they are embedding AI into the heart of their operations to unlock efficiencies and personalise customer experiences.
Banks, for instance, are using generative AI to help detect, manage, and prevent cyberattacks while ensuring a smooth cross-border payment experience for customers. In Asia, cross-border remittances have long been a fixture, and more money is moving across borders than ever. In ASEAN countries alone, cross-border digital payment volumes reached US$707 billion in 2021 and are projected to hit US$1.7 trillion by 2025, according to the report The Dynamic Nature of Cross-border Payments in Asia by Kapronasia in collaboration with Finastra.
To combat financial crimes such as money laundering and fraud, banks are analysing these transactions at scale, a task that generative AI can help streamline.
Banks must also navigate strict regulations that affect their productivity, capital, and growth. Penalties for non-compliance or customer data breaches through digital banking are exceptionally high in countries such as Malaysia.
To unlock AI’s full potential, businesses should build strong operational security, focus on human-centred AI design, and establish trust. Adopting a responsible governance model — one that prioritises transparency, ethics, and security from the outset — offers organisations a foundation for deploying AI responsibly at scale.
Protecting customers’ sensitive data and ensuring secure transactions will not be possible without the right skills. As banks update their legacy core systems to unlock generative AI’s potential, they must simultaneously prioritise workforce readiness by bridging skills gaps, aligning leadership, and fostering adaptability. AI implementation isn’t just a technology challenge; it’s also a people challenge. But it’s one that financial enterprises can overcome by preparing now to fully harness generative AI.
