How Agentic Data Management is transforming financial services in APAC

As financial institutions across APAC generate and consume ever-growing volumes of data, traditional approaches to data management are coming under increasing pressure. Growing regulatory expectations, expanding AI initiatives and rising business demand require organizations to rethink how they manage, govern and derive value from enterprise data.

Agentic data management is emerging as a new operating model that combines AI-driven automation with human oversight to help organizations manage data more efficiently and at greater scale.

In this Q&A, Bertie Haskins, Capco's APAC Head of Data, discusses how agentic data management is reshaping data operations across the region, where financial institutions are seeing the greatest opportunities, and what they should prioritize as they prepare for the next generation of data management.

How do you view agentic data management in the context of financial services, and why is it becoming a priority for financial institutions across APAC?

Financial institutions today are managing significantly larger volumes of data than ever before. This is not just for regulatory reporting, but also to support AI, analytics and business decision-making. As data volumes and complexity continue to grow, traditional, manual approaches to data management are becoming increasingly difficult and expensive to scale.

Agentic data management represents the next evolution of data operations. It uses AI agents to automate activities across the data lifecycle from discovery and classification to lineage, data quality and governance, while keeping humans in the loop for oversight and decision-making. By reducing manual effort and embedding intelligence into data operations, organizations can improve efficiency, strengthen trust in data and free up teams to focus on higher-value activities.

This is particularly relevant in APAC, where financial institutions often operate across multiple markets with differing regulatory, privacy and data sovereignty requirements. Managing these complexities demands richer metadata, stronger governance and greater transparency around how data is classified, accessed and used. Agentic data management helps organizations meet these requirements more efficiently while building the trusted data foundations needed to support broader AI ambitions.

Ultimately, the opportunity is about moving beyond data management as a compliance exercise and treating it as a strategic capability that enables innovation, improves operational efficiency and delivers greater value from enterprise data.

How do you see agentic data management changing the way financial institutions manage data across the enterprise? 

The biggest change is that data management shifts from being largely manual and people-driven to becoming more intelligent, automated and scalable. Traditionally, many activities, from identifying data quality issues to maintaining metadata and resolving exceptions have depended on manual effort, making it difficult to keep pace with growing data volumes and business demands.

With agentic data management, AI augments the way these activities are performed. Rather than spending time on repetitive operational tasks, data teams can focus on setting policies, validating outcomes and addressing more complex business problems. This helps improve speed, consistency and scalability of data operations, along with helping financial institutions reduce the cost of managing data and demonstrate greater return on their data investments.

Perhaps the biggest shift is in mindset. Data management is no longer viewed purely as a control function that supports compliance. Instead, it becomes a strategic capability that delivers trusted data more efficiently across the enterprise, supporting regulatory requirements while also enabling AI, analytics and better business decision-making.

For APAC institutions, where data often needs to be managed across multiple regulatory jurisdictions, this more intelligent operating model can significantly reduce operational complexity while strengthening governance and consistency.

Traditional vs. Agentic data management

From what you’re seeing in the market, where are most banks on the maturity curve for agentic data management? What sets the leaders apart from the rest?

Maturity varies considerably across APAC, with institutions at different stages of their agentic data management journey. While some organizations are beginning to explore use cases, others, particularly more digitally mature institutions and those in markets such as Singapore are already piloting or implementing agentic capabilities within their data operations. Overall, however, many firms are still focused on strengthening the foundations required to scale these capabilities, including data governance, architecture and enterprise data management. 

What differentiates the leaders is the maturity of their underlying data foundations. Organizations that have invested in consistent data models, robust governance frameworks and well-managed metadata are far better positioned to automate data management processes and scale agentic capabilities across the enterprise.

The most successful institutions also take a focused, value-led approach – they prioritize high-impact use cases, establish strong governance and demonstrate measurable outcomes before expanding adoption more broadly. That disciplined approach creates a more sustainable path to enterprise-wide transformation.

 


In terms of the highest impact use cases for agentic data management, what do you see as the key opportunities for APAC financial services? 

We're seeing the strongest adoption in areas where organizations manage large volumes of complex data and need greater trust, transparency and operational efficiency. Payments, capital markets and wealth management are all strong examples of where agentic data management is already delivering value.

In payments, institutions are using agentic data management to improve the quality, consistency and traceability of transaction data across increasingly interconnected payment ecosystems. This enables faster issue resolution, strengthens operational resilience and gives organizations greater confidence in the data underpinning high-volume payment processing.

In capital markets, the challenge lies in managing fragmented data across trading, risk and regulatory reporting functions. Agentic data management helps create a more consistent and trusted view of enterprise data, enabling firms to respond more quickly to data issues while improving confidence in business and regulatory reporting.

In wealth management, the priority is creating a trusted, connected view of client, portfolio and market data. Agentic data management helps improve data consistency across these information sources, enabling advisors to make better-informed decisions while strengthening governance and delivering a more reliable client experience.

While these examples differ by business function, the underlying objective is the same: building trusted, well-governed data at scale.

What do you see as the biggest challenge financial institutions face in scaling agentic data management, and how can they do so while maintaining strong governance, regulatory compliance and human oversight?

The biggest challenge is building the right data foundations. Agentic data management depends on high-quality, well-governed data and clear business context. Without these foundations, organizations risk inconsistent outputs, poor decision-making and reduced trust in AI-driven processes.

For financial institutions, particularly in highly regulated markets, governance cannot be an afterthought. As organizations scale agentic capabilities, they need robust governance frameworks, consistent data models and clear policies that ensure AI operates in a transparent, auditable and compliant manner. Human oversight also remains essential, with data teams shifting from executing routine tasks to validating outcomes, managing exceptions and providing strategic direction.

The key is to take a pragmatic, value-led approach. Rather than attempting enterprise-wide transformation from day one, organizations should focus on high-impact use cases, establish strong governance and data foundations, and scale incrementally as confidence and capabilities mature. That approach allows firms to realize tangible business value while maintaining the trust, control and accountability that are essential in financial services.

Agentic data management is redefining how organizations manage and derive value from data. While the journey is still in its early stages, institutions that invest in the right foundations today will be better equipped to innovate, scale and compete in an increasingly AI-driven future. To find out more, contact us via the form below.

Ready to explore Agentic data management?

Whether you’re strengthening your data foundations or scaling enterprise AI, our experts can help you assess your current capabilities and define the right path forward.

Complete the form below, and one of our experts will be in touch.