Indonesia’s financial institutions shift from AI experimentation to data modernization in pursuit of operational AI

Senior banking and financial services executives discussed the industry's progress in modernization and AI readiness during a recent roundtable discussion by MongoDB and Multipolar in Jakarta.

Indonesia’s financial institutions are entering a more mature phase of AI adoption, with leaders from the industry increasingly recognizing that successful AI deployment depends less on the models themselves and more on the quality, connectivity and governance of the data beneath them.

That was one of the key conclusions from the MongoDB and Multipolar Executive Roundtable in Jakarta, where senior banking and financial services executives discussed the industry's progress in modernization and AI readiness.

As AI continues to evolve from pilot projects to operational deployments, financial institutions are shifting their attention towards the underlying technology environments required to support the next wave of innovation. Executives at the roundtable, which included C-level execs from the various banking and financial institutions in Indonesia, agreed that the conversation is no longer centered on what AI can do, but whether organizations have the right data foundations to enable AI to operate effectively and securely.

The discussion, moderated by Aaron Raj, CRN Asia Editorial Director, comes at a time when Indonesia's financial sector is experiencing rapid digital growth. Digital transactions in Indonesia reached US$14.8 billion in the first quarter of 2026, representing year-on-year growth of 37.69 per cent.

As transaction volumes continue to rise, banks and financial institutions face mounting pressure to modernize legacy systems while preserving the resilience and trust that customers expect.

A central theme throughout the discussion was the challenge of fragmented data. Many institutions continue to operate multiple systems across core banking, lending, trade finance and customer applications, creating silos that prevent the creation of a unified customer view.

According to Halim Hartono, Director of Business Development and Innovation at Multipolar, delivering personalized services and Customer 360 initiatives requires data to be accessible in real time across the organization. Without that connected foundation, AI initiatives risk being constrained by incomplete or inconsistent information.

During his presentation, Wei You Pan, Field CTO, Financial Services Industry at MongoDB, also shared that sprawling data impedes agentic AI deployments in most organizations with the financial services industry impacted the most. As such, businesses need an intelligent operational data platform that can unify real-time transactional databases, vector search, and AI agent integration into a single document-based system.

Specifically, MongoDB helps organizations everywhere move faster, innovate more efficiently, and simplify complex architectures through its integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval.

Accessing the right and accurate data

The roundtable also highlighted a growing industry belief that AI requires context rather than simply larger data volumes. As organizations explore more advanced use cases, including AI agents that can execute elements of business processes, access to trusted, relevant and contextual information becomes increasingly important.

The executives stressed that AI readiness is not merely about selecting the right large language model but ensuring that AI systems can access accurate data and business context when decisions are being made.

Security, governance and resilience remain major considerations. Financial institutions operate in highly regulated environments where system outages and service disruptions can directly affect customer confidence. For the executives, any modernization initiative must improve speed and intelligence without compromising operational stability, availability, or recovery capabilities.

Fraud prevention emerged as another priority. Executives observed that fraud is increasingly becoming an AI-versus-AI challenge, with bad actors using sophisticated tools to conduct attacks. To keep pace, institutions are exploring new approaches that combine traditional transaction analysis with behavioral, device and contextual data. Technologies such as embeddings and vector search were highlighted as potential tools to help identify suspicious activities and reduce false positives.

Perhaps most notably, the discussion revealed that Indonesian financial institutions are taking a gradual approach to AI transformation. Rather than pursuing full autonomy, many organizations are beginning with AI assistants that support employees before advancing towards automation, orchestration and intelligent workflows. Human oversight remains a critical requirement, particularly for regulated decision-making processes.

Given these responses from leading executives in the country, Indonesia's financial sector has moved beyond AI experimentation. The next phase of transformation will be defined by the ability of institutions to build connected, scalable and trusted data environments capable of supporting AI-driven innovation while maintaining regulatory compliance and customer trust. For many banks, the road to AI success now begins with data modernization.