As AI adoption in financial services moves beyond experimentation, institutions are increasingly focused on how to scale AI in ways that are compliant, reliable, and operationally effective. Meeting that challenge requires more than strong models. It depends on trusted infrastructure, governance, and disciplined implementation.
Against this backdrop, Rikkeisoft and the Swiss-Viet Economic Forum (SVEF) brought together voices from government, finance, legal, innovation, and technology in Zurich for a practical exchange on what it truly takes to deploy AI in regulated financial environments.
The Real Question Is No Longer “Whether” – It’s “How”
Across keynote presentations, roundtable sessions, and focused case exchanges, one theme emerged clearly: financial institutions have largely moved past the question of whether AI belongs in their operations. The pressing challenge now is how to implement it in ways that create trusted, repeatable, and auditable outcomes.
Topics that anchored the discussion included:
- AI applications in financial services operations
- Trusted digital infrastructure in regulated environments
- Compliance automation and regulatory technology
- KYC / AML workflow automation
- Fraud monitoring and transaction analysis
- Data integration and regulatory reporting
- Swiss-Vietnam collaboration opportunities in FinTech and innovation
Precision Over Creativity: What AI Actually Means in Regulated Workflows
In financial services, AI should not be approached as a technology trend in itself. The stronger framing is to apply AI to clearly defined operational pain points, particularly in workflows where accuracy, auditability, and control are non-negotiable.
According to Mr. Son Bui Director of Delivery Center – Global Market at Rikkeisoft, 04 principles have to be emphasized as especially relevant for institutions navigating this:
- AI in financial services should prioritize precision over creativity. In regulated workflows, value comes from consistency, structure, and auditability, not novelty. The ability to produce the same reliable output at scale, under compliance scrutiny, matters far more than generative capability alone.
- Human accountability remains central to AI deployment. AI can meaningfully improve speed and consistency, but final validation and decision-making still require human expertise. Institutions that treat AI as a replacement for professional judgment tend to encounter the same governance gaps they were trying to solve.
- Scaling AI starts with the right operational problem, not the technology itself. Organizations that begin with clear business pain points, and apply AI specifically to reduce friction and improve controls, consistently outperform those that start with the model and work backward.
- AI augments human expertise rather than replaces it. The strongest outcomes emerge from combining machine efficiency with domain judgment. Neither alone is sufficient in a regulated environment.
Governance and Infrastructure: The Foundation That Makes Scale Possible
Technical capability is only part of what makes AI deployable at scale in financial services. Governance and trusted infrastructure are equally foundational, and often the deciding factors in whether AI initiatives move from pilot to production.
Across the dialogue, Mr. Son Bui and panelists converged on 03 key priorities:
- Trusted digital infrastructure as a prerequisite, not an afterthought, for scaling AI in finance;
- Cross-border digital trust and alignment with international standards, particularly as institutions operate across jurisdictions;
- Stronger Switzerland-Vietnam collaboration as a practical pathway for knowledge exchange and co-development in financial technology.
The operational use cases discussed in the roundtable session included compliance workflow automation, enterprise data management, and regulatory reporting, grounded these priorities in concrete institutional challenges rather than theoretical concerns.
What This Means for Financial Institutions Moving Forward
The path from AI experimentation to production-scale deployment is not primarily a technical problem. It is a governance, infrastructure, and implementation discipline problem. Institutions that recognize this early tend to build more resilient AI programs, ones that can withstand regulatory scrutiny, integrate into existing workflows, and deliver consistent value over time.
Cross-sector and cross-border collaboration, of the kind explored between Switzerland and Vietnam, also points to a broader opportunity: that the institutions and technology partners best positioned to lead in regulated AI will be those that invest in shared standards, mutual trust, and long-term cooperation, not just capability.
Why Implementation Experience Changes the Conversation
For Rikkeisoft, the Zurich dialogue was more than a speaking opportunity, it was a reflection of where the company stands in the market. With a strategic focus on Finance/BFSI and a track record built on delivery in high-requirement environments, Rikkeisoft brings something that is increasingly rare in AI conversations: implementation credibility. The ability to not just discuss what AI should do in regulated settings, but to show how it has been done, at scale, with governance built in from the start. As financial institutions look for technology partners who can bridge the gap between ambition and operational reality, that distinction matters.
Photo credit: Rikkeisoft.
