Top 5 Jobs in Financial Services That Are Most at Risk from AI in Viet Nam - And How to Adapt

Vietnam's banking and finance scene is already being reshaped by a national AI push - ranked 6th on the WIN World AI Index and backed by new rules in the DTI Law - so routine tasks like KYC checks, loan processing and fraud detection are prime for automation across banks and microfinance providers; see the big-picture view in the State of AI in Vietnam (2025) for why infrastructure, talent drives and policy momentum matter.

Local banks are pairing cloud modernization with embedded AI to speed decisioning and improve customer journeys, a trend explored in Temenos' take on Vietnam's banking transformation, and that shift means many entry-level, rule-based roles will change fast unless employees and employers reskill.

Practical upskilling - focused on prompts, AI tools and workplace application - can bridge the gap; Register for the Nucamp AI Essentials for Work bootcamp to follow a 15-week path to real-world AI skills for non-technical staff looking to move from routine processing into oversight, prompt design and AI-augmented roles.

Selection of the top five at‑risk roles focused on how often a job relies on repeatable, rule‑based work, the pace of digital adoption in Vietnam's market and the regulatory outlook that shapes which processes banks automate first; criteria drew on industry analysis such as Deloitte Southeast Asia Banking & Capital Markets insights for risk and governance factors and on market signals about adoption and scale from GFT Vietnam digital banking transformation review.

Practical indicators used were: prevalence of routine transactions (KYC, data entry, basic underwriting), digital channel penetration (over 80% smartphone ownership and rapid mobile payments growth), and pockets of regulatory encouragement for tech-driven change - each weighted to reflect Vietnam's dual reality of fast digital uptake (32 million active e‑wallets cited in local industry reporting) alongside legacy operations that make certain entry‑level jobs uniquely automatable; the result is a short, actionable list of roles most exposed to automation and where targeted reskilling can have immediate impact.

"What gets me most excited right now is the most relaxed regulations from regulators." - Andy Nguyen Tran, CFO of Zalopay

Bank tellers and front‑line retail bankers in Việt Nam are among the most exposed to automation because so much of their day is repeatable counter work - cash handling, form filling and basic account opening - that can be replaced by eKYC, AI/OCR, RPA and self‑service channels; FPT IS lays out how AI/OCR and RPA are already reshaping branch counters and speeding routine processing.

The change is visible on the ground: some lenders report that “nearly 500 automation robots have taken over simple tasks,” freeing staff for higher‑value work but also driving payroll cuts and branch consolidation as digital account opening and automated loan workflows scale.

Banks and employees should therefore treat teller roles as a pivot point - moving from transaction processing to advisory, sales and tech‑support for customers - because branch interactions that remain will be higher‑value and relationship driven.

For anyone tracking where jobs are shrinking and where reskilling matters most, see reporting on recent bank headcount shifts and the broader digital push in Vietnamese banking.

“Tellers today are not just counting cash or inputting data; they are supposed to be financial consultants, salespeople and tech supporters who help customers optimize their cash flow.” - VnExpress

Data-entry, back‑office processing and KYC clerks are squarely in AI's crosshairs in Việt Nam because identity verification, document processing and repetitive screening are now both highly automatable and tightly regulated: banks that adopted eKYC report cutting account opening “from multiple days to minutes,” replacing stacks of forms with AI/OCR and biometric checks and shifting much of the routine workload out of human queues - see how eKYC adoption in Vietnam is speeding onboarding.

At the same time, tighter rules on biometric verification (face and fingerprint checks for transfers above VND10 million and other thresholds) are forcing institutions to centralise digital identity flows and invest in secure pipelines, which reduces some manual checks but raises new compliance, privacy and fraud‑management tasks that machines alone don't solve; banks need scalable human‑in‑the‑loop teams to handle exceptions and KYB complexity.

For workers, the practical takeaway is clear: move from raw data entry to exception handling, model governance and customer‑facing verification support - roles that protect privacy, reduce false positives and keep the fraud losses and customer drop‑offs in check.

“If the onboarding process is too complicated, customers will abandon the service.” - Asia Banking & Finance

Entry-level financial analysts, junior accountants and reporting clerks are seeing the “boring” parts of their jobs - data entry, reconciliations and routine report generation - shift to AI, which can speed up data processing, reduce errors and churn out draft narratives and audit trails in seconds; see the CPA Journal's analysis of how AI assists accountants in reading standards and pulling insights.

That automation frees room for higher-value work: variance analysis, scenario modelling, model governance and narrative-driven business partnering, and finance teams that learn to use tools like Microsoft Copilot for Finance will turn tedious monthly closes into time for strategic advice and risk spotting.

For Vietnamese firms the implication is clear: keep hiring early-career talent but change the job description - prioritise AI literacy, critical thinking and communication over repetitive bookkeeping - and build training paths so junior staff graduate from running spreadsheets to owning explanations and exception-handling that machines can't replicate (examples of Vietnam-focused efficiency gains are summarised in sector guides on how AI is helping financial services in Việt Nam).

“AI is transforming the purchasing team's ability to analyze contracts, speeding up the review process and freeing up time for strategic work.” - Hugh Cumming, Vena Solutions

Loan-processing officers and basic credit underwriters are squarely in the path of automation: the repetitive work of pulling documents, reconciling income and applying rule-based scorecards is being replaced by machine‑learning pipelines and GenAI that can ingest rent and utility records, mobile payment trails and even retail‑purchase patterns to produce faster, more granular decisions - think hundreds or thousands of variables rather than a single score - and examples show underwriting that was once measured in days can be compressed to minutes via automated scoring and OCR‑driven document handling (machine learning credit scoring techniques for faster underwriting).

That efficiency opens lending to “thin‑file” borrowers but raises sharp compliance tradeoffs: regulators are forcing lenders to search for less‑discriminatory alternatives, justify input variables and document how adverse‑action reasons are generated, so human oversight and model governance become core skills for remaining staff (CFPB fair‑lending guidance on advanced credit scoring models).

In Việt Nam, this means loan officers should pivot from routine checks to exception‑handling, explainability, and customer communication - translating a model's output into a fair, auditable decision - so that automation lifts throughput without becoming a black box for customers or regulators (how AI is helping financial services in Việt Nam to cut costs and improve efficiency).

Routine compliance and regulatory reporting operatives in Việt Nam are squarely in the spotlight as banks swap manual SAR/STR work for AI‑driven transaction monitoring: machine learning can cut alert volumes dramatically but also creates a new frontline - model governance, rule coverage and escalation design - that teams must master (some programmes still see 95–99% false positives under legacy rules, a reminder that automation without tuning simply buries analysts).

Vietnamese institutions moving from static rules to hybrid ML systems should follow risk‑based best practices - start with a thorough rules coverage assessment, map red‑flag scenarios to monitoring rules and build clear escalation paths - so that alerts become high‑value intelligence rather than noise; see NICE Actimize rules coverage assessment guidance and the Sanctions.io transaction monitoring implementation guide for AML compliance.

The practical career pivot for operatives is therefore clear: shift from checkbox reporting to roles that own model validation, explainability, SAR quality control and cross‑team data management, plus continuous training so human judgement stays central as systems evolve.

Practical next steps for Việt Nam are straightforward: treat upskilling as a strategic priority and design short, employer‑backed pathways that match market demand - Talentnet's workforce framework shows why making skills development a leadership priority and partnering with training providers is essential, because the average skill half‑life is now under five years; follow that by targeting analytical thinking and core AI literacy which Vietnamese workers already value highly.

Employers should give paid learning time, cover costs and map roles to new “human‑in‑the‑loop” tasks (model governance, exception handling and customer explainability), since Economist Impact finds nearly 80% of employees say upskilling improves pay yet many cite poor clarity on what to learn and lack of time; businesses also benefit from clear, short curricula and ecosystem partners to scale reskilling.

For individuals, pick focused, practical courses that teach prompt design, AI tools and workplace application - Nucamp's 15‑week AI Essentials for Work bootcamp offers a direct path from routine tasks to oversight and prompt‑crafting.

Start small, measure learning outcomes, and tie reskilling to concrete role changes so automation raises productivity instead of simply cutting headcount.

“AI is becoming a sustainable resource for businesses - abundant, essential and scalable.” - General Director of Microsoft Việt Nam Nguyễn Quỳnh Trâm