Home to over 60% of the global population, the Asia Pacific region is at the forefront of digital transformation – and at a turning point. The Asian Development Bank forecasts a USD 1.7T GDP boost by 2030, but only if regulation keeps pace with innovation. In 2025, that alignment is taking shape: regulators across the region are actively crafting policies and platforms to scale innovation safely and steer it toward public good. Their focus spans global AI rules, oversight of critical tech in BFSI, sustainable finance, green fintech, and frameworks for digital assets.
Here’s a look at some of the regulatory influences on the region’s BFSI organisations.
Click here to download “Greener, Smarter, Safer: BFSI’s Regulatory Agenda” as a PDF.
The Ripple Effect of Global AI Regulation on APAC Finance
The EU’s AI Act – alongside efforts by other countries such as Brazil and the UK – signals a global shift toward responsible AI. With mandates for transparency, accountability, and human oversight, the Act sets a new bar that resonates across APAC, especially in high-stakes areas like credit scoring and fraud detection.
For financial institutions in the region, ensuring auditable AI systems and maintaining high data quality will be key to compliance. But the burden of strict rules, heavy fines, and complex risk assessments may slow innovation – particularly for smaller fintechs. Global firms with a footprint in the EU also face the challenge of navigating divergent regulatory regimes, adding complexity and cost.
APAC financial institutions must strike a careful balance: safeguarding consumers while keeping innovation alive within a tightening regulatory landscape.
Stepping Up Oversight: Regulating Tech’s Role
Effective January 1, 2025, the UK has granted the Financial Conduct Authority (FCA) and Bank of England oversight of critical tech firms serving the banking sector. This underscores growing global recognition of the systemic importance of these providers.
This regulatory expansion has likely implications for major players such as AWS, Google, and Microsoft. The goal: strengthen financial stability by mitigating cyber risks and service disruptions.
As APAC regulators watch closely, a key question emerges: will similar oversight frameworks be introduced to protect the region’s increasingly interconnected financial ecosystem?
With heavy reliance on a few core tech providers, APAC must carefully assess systemic risks and the need for regulatory safeguards in shaping its digital finance future.
Catalysing Sustainable Finance Through Regional Collaboration
APAC policymakers are translating climate ambitions into tangible action, exemplified by the collaborative FAST-P initiative between Australia and Singapore, spearheaded by the Monetary Authority of Singapore (MAS).
Australia’s USD 50 million commitment to fintech-enabled clean energy and infrastructure projects across Southeast Asia demonstrates a powerful public-private partnership driving decarbonisation through blended finance models.
This regional collaboration highlights a proactive approach to leveraging financial innovation for sustainability, setting a potential benchmark for other APAC nations.
Fostering Green Fintech Innovation Across APAC Markets
The proactive stance on sustainable finance extends to initiatives promoting green fintech startups.
Hong Kong’s upcoming Green Fintech Map and Thailand’s expanded ESG Product Platform are prime examples. By spotlighting sustainability-focused digital tools and enhancing data infrastructure and disclosure standards, these regulators aim to build investor confidence in ESG-driven fintech offerings.
This trend underscores a clear regional strategy: APAC regulators are not merely encouraging green innovation but actively cultivating ecosystems that facilitate its growth and scalability across diverse markets.
Charting the Regulatory Course for Digital Asset Growth in APAC
APAC regulators are gaining momentum in building forward-looking frameworks for the digital asset landscape. Japan’s proposal to classify crypto assets as financial products, Hong Kong’s expanded permissions for virtual asset activities, and South Korea’s gradual reintroduction of corporate crypto trading all point to a proactive regulatory shift.
Australia’s new crypto rules, including measures against debanking, and India’s clarified registration requirements for key players further reflect a region moving from cautious observation to decisive action.
Regulators are actively shaping a secure, scalable digital asset ecosystem – striking a balance between innovation, strong compliance, and consumer protection.
Ecosystm Opinion
APAC regulators are sending a clear message: innovation and oversight go hand in hand. As the region embraces a digital-first future, governments are moving beyond rule-setting to design frameworks that actively shape the balance between innovation, markets, institutions, and society.
This isn’t just about following global norms; it’s a bold step toward defining new standards that reflect APAC’s unique ambitions and the realities of digital finance.
“AI Guardrails” are often used as a method to not only get AI programs on track, but also as a way to accelerate AI investments. Projects and programs that fall within the guardrails should be easy to approve, govern, and manage – whereas those outside of the guardrails require further review by a governance team or approval body. The concept of guardrails is familiar to many tech businesses and are often applied in areas such as cybersecurity, digital initiatives, data analytics, governance, and management.
While guidance on implementing guardrails is common, organisations often leave the task of defining their specifics, including their components and functionalities, to their AI and data teams. To assist with this, Ecosystm has surveyed some leading AI users among our customers to get their insights on the guardrails that can provide added value.
Data Security, Governance, and Bias
- Data Assurance. Has the organisation implemented robust data collection and processing procedures to ensure data accuracy, completeness, and relevance for the purpose of the AI model? This includes addressing issues like missing values, inconsistencies, and outliers.
- Bias Analysis. Does the organisation analyse training data for potential biases – demographic, cultural and so on – that could lead to unfair or discriminatory outputs?
- Bias Mitigation. Is the organisation implementing techniques like debiasing algorithms and diverse data augmentation to mitigate bias in model training?
- Data Security. Does the organisation use strong data security measures to protect sensitive information used in training and running AI models?
- Privacy Compliance. Is the AI opportunity compliant with relevant data privacy regulations (country and industry-specific as well as international standards) when collecting, storing, and utilising data?
Model Development and Explainability
- Explainable AI. Does the model use explainable AI (XAI) techniques to understand and explain how AI models reach their decisions, fostering trust and transparency?
- Fair Algorithms. Are algorithms and models designed with fairness in mind, considering factors like equal opportunity and non-discrimination?
- Rigorous Testing. Does the organisation conduct thorough testing and validation of AI models before deployment, ensuring they perform as intended, are robust to unexpected inputs, and avoid generating harmful outputs?
AI Deployment and Monitoring
- Oversight Accountability. Has the organisation established clear roles and responsibilities for human oversight throughout the AI lifecycle, ensuring human control over critical decisions and mitigation of potential harm?
- Continuous Monitoring. Are there mechanisms to continuously monitor AI systems for performance, bias drift, and unintended consequences, addressing any issues promptly?
- Robust Safety. Can the organisation ensure AI systems are robust and safe, able to handle errors or unexpected situations without causing harm? This includes thorough testing and validation of AI models under diverse conditions before deployment.
- Transparency Disclosure. Is the organisation transparent with stakeholders about AI use, including its limitations, potential risks, and how decisions made by the system are reached?
Other AI Considerations
- Ethical Guidelines. Has the organisation developed and adhered to ethical principles for AI development and use, considering areas like privacy, fairness, accountability, and transparency?
- Legal Compliance. Has the organisation created mechanisms to stay updated on and compliant with relevant legal and regulatory frameworks governing AI development and deployment?
- Public Engagement. What mechanisms are there in place to encourage open discussion and engage with the public regarding the use of AI, addressing concerns and building trust?
- Social Responsibility. Has the organisation considered the environmental and social impact of AI systems, including energy consumption, ecological footprint, and potential societal consequences?
Implementing these guardrails requires a comprehensive approach that includes policy formulation, technical measures, and ongoing oversight. It might take a little longer to set up this capability, but in the mid to longer term, it will allow organisations to accelerate AI implementations and drive a culture of responsible AI use and deployment.
