The lean AI plan for action at VietBank

As a veteran of IT leadership, and just over two years into his current role as VietBank CIO, NghiaTran has rebuilt a strategic engine by not trying to out-spend the competition but by investing in AI-driven customer intelligence, like behavioral analytics and CRM integration. And since sensitive banking data can’t leave the building, flagship AI innovations, like their smart office tracking system (SOTs) and intelligent management system (IMS), were built entirely in-house using open-source components including a self-hosted LLM, rather than tools procured from enterprise vendors.

Delivered in just a few months on a lean budget, says Tran, SOTs cut document approval cycles by 35%, earned VietBank a CIO ASEAN Innovation Award in 2025, and drew an invitation from the Vietnamese government to present at last year’s National Digital Governance Conference.

From conceiving and building AI initiatives in-house to urgently deploying AI instead of waiting for perfect data, Tran has a vision of how to progress that makes the most sense to the business. “If we keep waiting for perfect data, we fall behind our competitors,” he says. The means by which to measure success, he adds, is through culture, in that even when hardware costs are skyrocketing as AI chip demand surges globally and business units feel the strain, giving people autonomy and room to grow make their work and place worth sticking around for. 

What Tran is building at VietBank with a lean team, a clear plan, and an insistence for action, is a reminder that clarity and execution matter more than immediate and impatient scaling.

“My professional focus is on building a resilient technology foundation, advancing cyber maturity, and aligning with the complex IT ecosystem with business strategy and regulatory expectation,” he says. “My role is to ensure technology isn’t only innovative, but also secure, scalable, and directly tied to business value.”

Tran also details cybersecurity as the sector’s most underappreciated risk, keeping pace with neobanks, and adapting to change. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.

On AI enabling diversification: I deployed agentic AI for the bank, which helps to automate and optimize critical processes such as document processing, approvals, and reporting to leadership with reduced manual operation, increased transparency, and greater data security within the internal environment.

Our IT targets value across efficiency, control, security, and scalability, and that’s my role. My target for IT support for the business is to improve information retrial, and write the quality and consistency of internal reporting and decision support. And from that, I and my team try to develop the technology that’s enables the business to function, and to help them to maximize their efforts.

The more we understand customers, the better we can serve them. And we can redeem a lot of value-added service, confidence, safety, and security with AI.

On inward-facing AI: Data accountability is a very important principle in banking, considering all the sensitive information, security files, and finance statements. So material must remain fully within the bank’s control. For me, AI is of great value so we chose to develop in-house with a native model. We could make the banking provide intelligence and trust, and a smart office system was designed so documents and the entire model stay within the bank environment, which protects confidence, avoids external token costs, and aligns with state regulations about the data profession.

This approach gives us the flexibility to innovate while maintaining full control over our data and architecture. The smart office tracking system (SOT) we deployed, after only a few months and using a small amount of budget, keeps sensitive information on-prem. Using agents, SOT can summarize and optimize documentation, while IMS is multifaceted and we have an internal assistant to look up the regulation procedure, support the operation, and mitigate data-related risk. We apply it to process management, like automation, approval process management, and asset control management, integrating a holistic ecosystem.

On cybersecurity: Banking tech is very serious about cybersecurity, and the way I approach it is to have a multi-layer and proactive approach to defense. We have a red team testing inside and outside for vulnerabilities, and we have a blue team to operate with the National Cybersecurity Agency. We also have a consulting team to stay on top of new trends. With AI, you can assess cybersecurity very easily, but we have to be proactive, especially when attackers use AI to attack the system, most notably in Vietnam, which is one of the top global target for cyberattacks.AI-powered threats are moving faster than legacy security architectures can handle. My approach is multi-layered, but a broader concern is systemic since a supply chain attack on one bank can cascade throughout the entire financial system.

On vision 2028: I see the same questions will be asked in many disciplines and panels. We’re waiting for the perfect data platform, or how to run AI, because some will say that data must be well structured before it’s run through AI. For me, I make choices based on the business case. If an AI approach is successful, scalable, and we receive good feedback, we can deploy for the whole bank. That’s the safe approach for banking. I used to work in consulting so I always advise that if you have enough money and resources, you should do the analysis and AI transformation smartly.

With budgets and finance, hardware costs have dramatically increased and this could greatly impact your strategy, especially when it comes to investment to enhance and modernize infrastructure. HR will also be crucial. You develop talent but refining the way to keep it for the long term is very difficult. With teamwork, you have a team for people to study. Let them use that to develop their career path.