The Complete Guide to Using AI as a Finance Professional in Viet Nam in 2025

For finance professionals in Viet Nam in 2025, AI is no longer a distant trend but a practical tool for faster, safer finance work: national momentum has put Vietnam 6th on the World AI Index and backed a “Make in Vietnam” push for Vietnamese‑language models like CATI‑VLM, which enable tasks from automated VN‑language invoicing and KYC to real‑time fraud detection, dynamic credit scoring and smarter loan processing (see the State of AI in Vietnam for 2025 for the big picture).

At the same time, evolving rules - DTI, the forthcoming PDP and Decree 13 - mean teams must balance innovation with compliance, so pairing domain knowledge with applied AI training is key; practical upskilling options such as the AI Essentials for Work bootcamp - Nucamp registration can help finance teams adopt AI responsibly and productively.

“Vietnam's biggest ‘superpower' is family values and respect for education. Vietnamese people excel in STEM fields, especially math and science. This makes Vietnam the second-largest supplier of software engineers in the world – a fact that few people know about. With this potential, we believe that Vietnam is an ideal place for NVIDIA to develop R&D centers and build a strong AI ecosystem here.”

Vietnam's AI economy in 2025 looks less like a fad and more like a fast-growing industry: independent estimates cluster around a market size of roughly USD 0.5–0.75 billion for 2023–24 with double‑digit growth ahead, for example Nexdigm's Vietnam AI market outlook puts 2024 at USD 753.4 million with a 14.96% CAGR to 2030 and Credence Research projects a similar ~15–16% CAGR into the next decade; together these figures signal steady commercial demand rather than a one‑off spike.

The ecosystem's momentum is visible on the ground - hundreds of AI startups (the Vietnam AI Economy 2025 report counts about 765 AI and ML startups, roughly one quarter of all tech startups) and major corporate hubs in Hanoi and Ho Chi Minh City - yet structural gaps remain (a large pipeline of general IT graduates but only a few thousand deep AI specialists and limited domestic supercompute capacity).

For finance teams this means a crowded vendor landscape, rising local capability for VN‑language models and fintech use cases, and a long‑range prize: national studies estimate AI could add USD 120–130 billion to GDP by 2040 if talent, data and policy bottlenecks are addressed - making measured investment and upskilling today a competitive imperative.

See Nexdigm's market outlook, the Vietnam AI Economy 2025 analysis, and Credence Research's projection for the underlying data and scenarios.

Regulation in Viet Nam in 2025 is moving from patchwork to a more structured regime that every finance team must treat as infrastructure: the newly passed Law on Digital Technology Industry (DTI Law) introduces a risk‑based AI classification, lifecycle risk management and mandatory labeling for systems that interact with people or generate digital products, and tasks the Ministry of Science and Technology with lists and sectoral guidance - in short, AI tools used for invoicing, KYC or credit scoring will need clear identification and documented controls (see the DTI Law overview).

Alongside the DTI Law, the Law on Data (effective 1 July 2025) creates formal rules for data classification, cross‑border transfer conditions and a national data architecture that tighten how customer and payment data can be handled, audited and moved abroad (see the Data Law summary).

Existing personal data rules still matter: Decree 13 (the PDPD, in force since July 2023) already requires DPIAs/TIAs, cross‑border impact assessments and data‑centric governance, and emerging PDP legislation signals even tougher consent rules, frequent DPIA/TIA updates and larger fines - with explicit extraterritorial reach for vendors serving Vietnamese users (read the PDP analysis).

For finance professionals the practical checklist is simple but serious: classify data, run DPIAs/TIAs before piloting models in a sandbox, require vendor assurances on localization and explainability, and treat AI labeling and audit trails as non‑negotiable parts of any production deployment.

For finance professionals in Viet Nam in 2025, AI is already a toolbox of high‑impact use cases - start with eKYC to streamline onboarding (major banks report cutting account opening from days to minutes), layer in real‑time fraud detection and AML screening to catch anomalies as transactions flow, and deploy dynamic credit scoring to extend responsible lending to thin‑file customers and reduce NPLs; VN‑language models like CATI‑VLM and local AI stacks make invoice automation, VN‑language OCR and document parsing practical for accounting teams, while AI Agents can run routine workflows, generate internal reports and free senior staff for strategic tasks (see the national overview in The State of AI in Vietnam for 2025 and examples of eKYC adoption).

Practical pilots should pair automation with strict controls: use regulatory sandboxes, live DPIAs, and layered liveliness/deepfake detection to guard eKYC flows - deepfakes remain an evolving threat to biometric checks and demand active mitigations.

When chosen and governed well, these use cases translate into faster customer journeys, lower operating costs, measurable credit expansion and a smoother audit trail - in short, AI that helps finance teams work smarter, not harder.

Data governance and sourcing are the backbone of any safe, scalable AI deployment in Viet Nam - especially for finance teams that handle payments, customer IDs and credit histories - because the regulatory stack now treats data flows as national infrastructure rather than a back‑office choice.

Start with the basics: Decree 13 (the PDPD) requires DPIA/TIA filings for processing or transferring Vietnamese personal data (a 60‑day filing window and inspection by the Ministry of Public Security's A05), while the Law on Data introduces “important” and “core” data categories that trigger stricter cross‑border controls and periodic risk assessments; the recently passed Personal Data Protection Law adds revenue‑based penalties (and sectoral rules for finance) that can reach into meaningful percentages of turnover.

Practically that means map and classify every dataset, treat VN‑language customer records and invoices as potential “important/core” assets, encrypt at rest and in transit, insist on vendor contract clauses that cover localization and TIA/DPIA support, and run regular risk re‑assessments - otherwise a noncompliant service can be effectively excluded from the market (recall the Telegram blocking in May 2025).

Useful implementation guides include the ITIF summary on cross‑border rules and impact assessments, Hogan Lovells' overview of the Law on Data and the National General Database, and the Vietnam Briefing note on the draft implementation decree that spells out core/essential criteria for transfers and approvals.

Upskilling finance teams in Viet Nam in 2025 means choosing a pragmatic mix of theory, tools and focused practice: start with AI literacy sessions that explain finance use cases (eKYC, AP automation, dynamic credit scoring) and the regulatory landscape, then pair that with hands‑on tool practice and a single, measurable pilot - pick one process (for example, accounts payable or management reporting) to automate and show a clear time or error reduction.

Three practical paths work well together: self‑paced courses to build foundation knowledge, vendor-led training to learn platform specifics, and finance‑focused accelerators that combine strategy with applied labs; GrowCFO's breakdown of these paths is a useful checklist for deciding which combo fits team capacity and risk appetite.

Make use of Vietnam‑specific programs and public initiatives - national targets to recruit elite AI experts and expanded AI education (see the State of AI in Vietnam for 2025) mean more local training, datasets and partnerships are becoming available - so prioritize Vietnamese‑language NLP tools and role‑specific curricula to avoid costly rework later.

Finally, embed change management: short sprints, executive sponsorship, and clear KPIs turn classroom learning into lasting operational value.

Funding AI in Viet Nam in 2025 is a pragmatic blend of market capital, targeted incentives and carefully structured public‑private support: foreign direct investment remains a primary channel (FDI stock was about USD 297 billion at end‑2023) and private investors can tap the country's three investment models - traditional private, public and PPP - to structure deals that protect cash flow and bankability (see the Vietnam project finance guide).

For AI projects the Law on Investment and sectoral lists mean software, R&D and high‑tech firms often qualify for meaningful tax breaks - think tax holidays, import‑duty exemptions and preferential CIT rates (10–17%, with some hi‑tech projects eligible for 10% for 15 years) as laid out in Acclime's incentives guide - yet the 2023 adoption of global minimum tax rules has made some tax incentives less potent, so model those savings conservatively.

Practical funding routes include direct FDI and strategic PPPs for larger platforms, together with development finance: the U.S. DFC and other development lenders are active in Vietnam (DFC has several active commitments), and pilot mechanisms like DPPA-style programs show how donor‑backed facilities can de‑risk tech investments in priority areas.

A sharp, memorable reality - policy support can open a wide revenue window, but Vietnam rarely issues blanket government guarantees, so lenders will still demand rock‑solid cash‑flow projections and local compliance (data localization, DPIAs) before committing capital.

For finance leaders, the takeaway is to combine incentive mapping, conservative tax modelling and a mixed funding stack (FDI, concessional finance, and partner‑led PPP) to make AI pilots bankable and scalable in the Vietnamese market.

Operationalizing AI across Vietnamese finance teams now means turning model risk management into a living, monitored system: the State Bank of Vietnam (SBV) is rolling out monitoring measures and tools to assess banks' compliance with Basel‑III risk rules, so institutions deploying AI for credit, scoring or liquidity forecasts must pair model inventories and observability with industry‑scale monitoring (see the SBV compliance overview).

Practical controls start with use‑case centric governance (treat an AI‑driven credit decision, not just the algorithm, as the unit of risk), strong vendor oversight for external scoring engines, and automated drift detection and retraining pipelines so models don't decay silently - in short, make model monitoring the smoke alarm that must sound before a small spark becomes a system‑wide failure.

Addressing Vietnam‑specific gaps means supporting SBV calls for a shared SME/LGD data repository and standardized stress‑test scenarios so ICAAPs are comparable across banks, while internal measures should include explainability checks, shadow‑model audits, clear escalation playbooks and targeted upskilling.

For a practical checklist of hidden model risks and mitigation techniques, see Forvis Mazars' guidance on lesser‑known MRM threats and Informaconnect's roadmap for moving from model‑centric to AI‑centric risk frameworks.

“This is one of the SBV's important orientations to improve governance efficiency, ensuring the safety of the banking system in the context that many new challenges are arising.”

Choosing vendors for AI in Viet Nam means treating procurement like a compliance-first bet: pick suppliers that can legally operate onshore, pass security audits, and accept Vietnamese‑law controls rather than hoping to retrofit contracts later.

Require a local legal entity or clear plans to establish one (the draft Decree on Data‑related Activities and the Law on Data make on‑the‑ground presence a baseline), insist the vendor's infrastructure and key technical staff meet the VN residency and certification criteria, and confirm any conditional financial safeguards - for some services the draft decree calls for a deposit of at least VND 5 billion (≈ US$200,000).

Contractually embed DPIA/TIA support, documented cross‑border risk assessments (the PDPD/Decree 13 require filings and 60‑day windows), data localization and encryption at rest/in transit, audit rights and breach notification SLAs, and clear DPA clauses that allocate controller/processor duties.

Operational checks matter too: limit who can change master supplier data, require role‑based access, and test the vendor in a regulatory sandbox where available.

For due diligence, use the Decree guidance on provider criteria and Vietnam's consolidated data law summaries to flag vendors that can't meet licensing, staffing or infrastructure rules early in the RFP stage (Draft Decree on Data‑related Activities - Vietnam Briefing (Vietnam data regulations), Vietnam data protection laws overview - DLA Piper).

“Vietnam's biggest ‘superpower' is family values and respect for education. Vietnamese people excel in STEM fields, especially math and science. This makes Vietnam the second-largest supplier of software engineers in the world – a fact that few people know about. With this potential, we believe that Vietnam is an ideal place for NVIDIA to develop R&D centers and build a strong AI ecosystem here.”

The roadmap for finance professionals in Viet Nam is straightforward: move from curiosity to controlled pilots, embed compliance, and ramp up skills - fast. Start with a tightly scoped pilot (accounts payable automation, eKYC or dynamic credit scoring) inside a regulatory sandbox or controlled environment, pair every pilot with a DPIA/TIA and data classification so the Law on Data and Decree 13 requirements are met, and treat explainability and vendor localization as non‑negotiable; these pragmatic first steps convert risk into demonstration value and guard market access while Vietnam builds capacity.

Next, invest in people and processes: short, role‑focused upskilling that teaches prompt design, tool workflows and governance turns pilots into repeatable operations (practical courses such as the AI Essentials for Work bootcamp help teams learn these workplace skills quickly).

Over the medium term, lock in funding and local partnerships - map tax and investment incentives, pursue PPP or FDI structures for scale, and favour vendors that can operate onshore and support audit trails so projects survive scrutiny and scale toward the national prize (Vietnam's AI economy could add USD 120–130 billion by 2040 if capacity gaps are closed).

Keep the playbook simple: pilot with clear KPIs, document DPIAs, train for tool‑first workflows, and insist on vendor SLAs and explainability - these steps turn policy momentum into measurable finance outcomes rather than another short‑lived hype cycle; see the Vietnam AI Economy 2025 report for the strategic case and this practical guide to Vietnam's regulatory sandbox and incentives for implementation.

“Vietnam's biggest ‘superpower' is family values and respect for education. Vietnamese people excel in STEM fields, especially math and science. This makes Vietnam the second-largest supplier of software engineers in the world – a fact that few people know about. With this potential, we believe that Vietnam is an ideal place for NVIDIA to develop R&D centers and build a strong AI ecosystem here.”