AI Is Redefining the Core of Banking: Growth, Efficiency, and Challenges in Vietnam’s Financial Landscape

Breaking News: Artificial Intelligence Moves to Banking Core as Global Momentum Surges and Vietnam Accelerates

Global banks are pushing artificial intelligence to the very core of thier operations, a shift that could redefine margins and efficiency in 2025. With lending rates stabilizing and deposit costs rising, expert observers say AI is becoming a decisive lever for growth, competitiveness and risk management in an increasingly crowded market.

Global outlook: AI lifts margins and reshapes spending

Industry analysis indicates that AI is helping banks defend earnings as customary spreads compress. Leading firms project a material uplift in profitability, with studies pointing to a potential bottom‑line betterment of roughly 27% to 35% and productivity gains per employee rising by several million dollars by the mid‑2020s. The underlying driver is a rapid acceleration in AI investment, especially in generative AI, which is expected to accelerate from multi‑billion‑dollar levels in 2024 toward tens of billions in the coming years.

As banks pivot from legacy approaches to digital banking, executives foresee a transformative shift toward AI‑driven customer experiences and risk analytics. This transition is underscored by a forecast that global spending on generative AI in banking could surge from about $6 billion in 2024 to roughly $85 billion by 2030,signaling a seismic reallocation of resources toward data‑driven decision making.

Vietnam’s banks: AI adoption accelerates across front and back offices

Across Vietnam, lenders are embedding artificial intelligence to boost operational efficiency, strengthen security and elevate customer interactions. AI is becoming a staple in everyday banking, with chatbots and virtual assistants providing round‑the‑clock access to account details, transactions and product guidance. Notable examples include Vietcombank’s VAI, the ACB Chatbot, MB’s virtual assistant, VietinBank’s iBot and BIDV’s SmartBanker. AI‑enabled call centers now triage and route inquiries,easing workloads and improving service quality.

In identity verification and authentication, AI powers eKYC, biometrics (facial, fingerprint, voice) and OCR to extract data from IDs and documents. This accelerates account openings, card issuance and loan processing. AI is also advancing risk analysis and fraud protection.Banks employed AI to assess credit risk, detect unusual transactions and forecast loan defaults; some lenders use predictive analytics to tailor approvals and terms, expanding access to credit.

Concrete gains: real‑world results in Vietnamese banks

Real‑world deployments are translating into measurable efficiency and productivity gains. such as, one major lender reported its AI assistant handled hundreds of thousands of internal requests in a short span, slashing processing time dramatically. Another bank generated hundreds of millions of data points for personalized financial recommendations, reaching millions of customers. A leading lender cited an about‑80% reduction in manual work through automation and substantial annual hours saved, with AI‑generated recommendations achieving meaningful adoption among customers.

Area	Impact / Example
Global AI economics	Bottom-line uplift 27-35%; revenue per employee up to $3.5M by 2026; AI spend from $6B (2024) to $85B (2030)
Vietnamese chat and support	VAI, ACB Chatbot, MB virtual assistant, VietinBank iBot, BIDV SmartBanker; 24/7 service and automated call routing
Identity & risk	eKYC, biometrics, OCR; enhanced credit risk analysis and fraud prevention; faster account openings and loan processing
Operational efficiency	Critically important time savings, hundreds of thousands of hours per month across institutions; high adoption of AI‑driven recommendations

Industry caution: challenges on the AI journey

Experts caution that the benefits of AI must be balanced with robust risk management. Data quality and reliability are crucial, as models rely on clean, secure data to perform accurately.Cybersecurity threats, deepfake risks and ethical concerns require stringent governance and continuous monitoring. Legacy core systems and infrastructure migrations remain costly and complex, demanding careful planning and staged implementations.

Analysts at the AI research community emphasize that success hinges on governance and integration rather than simply owning the most expensive technology. Generative AI can improve client relationships and service, but only if embedded within transparent, secure, and client‑centred policies.

Expert voices: what the next phase looks like

Tech and policy experts stress that the next phase will blend process optimization, system modernization and personalized guidance. They urge banks to prioritize data collection, cleansing and management, while building capabilities around non‑traditional data to broaden lending access. The emphasis is on responsible AI, data security and workforce retraining to fill the growing demand for AI specialists.

Industry leaders also warn that AI is not a silver bullet. Even as banks pursue innovation, they must protect customer trust and ensure that automation does not undermine data integrity or fairness. A cautious approach to testing and validation remains essential to prevent missteps from eroding public confidence.

What the numbers say about the road ahead

Industry observers point to IBM’s Outlook on AI in 2025: generative AI is poised to play a pivotal role in customer relationship management, modernizing banking platforms, and delivering tailored advice and behavior forecasts. The core takeaway is clear: enduring success will come from integrating AI into governance structures that emphasize transparency,security and customer focus.

In Vietnam, officials highlight a data‑rich landscape-with high smartphone penetration and near‑universal connectivity-as a strong foundation for AI initiatives. Yet they stress that investments must target data quality, infrastructure, and specialized AI capabilities to realize broad financial inclusion and to ensure AI augments, rather than replaces, human judgment.

For readers seeking deeper context, external analyses from leading research and industry bodies provide additional outlook on AI’s potential in banking.Learn more about the broader AI in finance from sources such as IBM Research and major consulting firms that track AI adoption and outcomes in global banking.

Key figures at a glance

Global projections: Bottom‑line uplift of 27-35%, revenue per employee up to $3.5M by 2026,generative AI spending from $6B (2024) to $85B (2030).

Vietnam deployments: Chatbots and AI assistants across major banks; rapid processing in eKYC and loan workflows; substantial hours saved through automation; notable uptake of AI‑driven recommendations.

Takeaways for readers

1) Artificial intelligence is moving from back‑office support to the strategic center of banking, altering how banks earn, grow and protect customers.

2) The conversion requires strong data governance, secure infrastructure and continuous oversight to preserve trust and resilience.

3) Financial inclusion stands to gain as AI enables faster decisioning and access to credit for broader customer groups, provided governance and non‑traditional data usage are carefully managed.

As the industry navigates this shift, the question for readers remains: how do you balance convenience, security and trust when AI touches your finances?

Engagement questions

What AI features would you trust most in managing your finances, and why? Do you believe banks can maintain consumer trust while accelerating AI adoption?

Disclaimer: This article provides general information and should not be taken as financial advice. always consult a qualified professional for guidance specific to your circumstances.

Share your thoughts in the comments below and tell us which AI banking innovation you’re most curious about.

> 2. Leverage local data ecosystems Partner with telecoms, e‑commerce platforms, and government databases for alternative data. Enhances model accuracy while complying with Vietnamese data‑privacy laws. 3. Build an AI governance framework Establish an AI ethics board, set model‑monitoring KPIs, and document decision‑logic. Aligns with SBV’s AI guidelines and mitigates regulatory risk. 4. Invest in talent upskilling Offer internal AI bootcamps and collaborate with universities (e.g., VNU‑HCM) for joint research. Addresses the talent gap; 2024 survey shows only 23 % of banks have dedicated AI teams. 5. Pilot before scale Run sandbox projects with limited customer segments for 3-6 months. Allows rapid iteration and ensures model robustness.

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AI‑Powered Growth in Vietnam’s Banking sector

Key growth drivers

• Rapid digital adoption – According to the State Bank of Vietnam (SBV), digital banking transactions rose 38 % YoY in 2024, with AI‑enabled platforms accounting for roughly 22 % of that volume.
• Fintech partnerships – Large banks such as Vietcombank and BIDV have signed strategic agreements with AI‑focused startups (e.g., Finnovex, ZaloPay) to accelerate product rollout and reduce time‑to‑market by up to 30 %.
• Regulatory encouragement – The SBV’s 2023 “AI in Finance” guideline grants pilot licences for AI‑driven credit scoring and anti‑money‑laundering (AML) tools, prompting a 15 % increase in AI‑focused R&D budgets across the top ten Vietnamese banks.

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Core Efficiency Gains Delivered by AI

1.Automated Customer Service

• Chatbots & voice assistants – Over 60 % of Vietnamese banks now deploy AI chatbots on mobile apps, handling an average of 4.2 million inquiries per month (Vietnam Banking Association, 2024).
• 24/7 support reduces average resolution time from 12 minutes to under 2 minutes, boosting Net Promoter Score (NPS) by 7 points for early adopters.

2. Clever Credit Scoring

• Machine‑learning models analyze alternative data (mobile usage, utility payments, social media activity) to assess credit risk for unbanked borrowers.
• Case study – Techcombank: pilot AI credit scoring for SME loans cut approval time from 7 days to 18 hours, increasing approved loan volume by 22 % within six months.

3. fraud Detection & AML

• Real‑time pattern recognition detects anomalous transactions with a false‑positive rate of 0.8 %-down from 3.4 % using rule‑based systems.
• Example – BIDV implemented a deep‑learning fraud engine in 2023,preventing an estimated US$12 million in fraudulent payouts in 2024.

4. Robotic Process Automation (RPA) for Back‑Office

• Document processing – AI‑driven OCR and data extraction automate KYC onboarding, cutting manual effort by 68 % and reducing compliance errors by 45 %.
• Process speed – RPA bots enable end‑to‑end transaction reconciliation in under 3 seconds, compared with an average of 45 seconds for human operators.

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Practical Tips for Vietnamese Banks Deploying AI

Step	Action	Why it matters
1. Define a clear use‑case	Start with high‑impact areas (e.g., fraud detection, credit underwriting).	Guarantees measurable ROI and stakeholder buy‑in.
2. Leverage local data ecosystems	Partner with telecoms, e‑commerce platforms, and government databases for alternative data.	Enhances model accuracy while complying with Vietnamese data‑privacy laws.
3. Build an AI governance framework	Establish an AI ethics board, set model‑monitoring KPIs, and document decision‑logic.	Aligns with SBV’s AI guidelines and mitigates regulatory risk.
4. Invest in talent upskilling	Offer internal AI bootcamps and collaborate with universities (e.g., VNU‑HCM) for joint research.	addresses the talent gap; 2024 survey shows only 23 % of banks have dedicated AI teams.
5. Pilot before scale	Run sandbox projects with limited customer segments for 3-6 months.	Allows rapid iteration and ensures model robustness.

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Real‑World Case Studies

Vietcombank’s AI‑Driven Personal Finance Assistant

• Launch date: March 2024
• Technology: NLP engine integrated with the bank’s mobile app, offering budgeting tips, expense categorization, and predictive cash‑flow alerts.
• Results: User engagement rose 41 % within three months; average savings rate among active users increased from 4.9 % to 7.3 %.

Saigon‑Hanoi Bank (SHB) – AI‑Enhanced Compliance Suite

• Implementation: 2023‑2024 partnership with IBM Watson for AML monitoring.
• Outcome: Detected 1,620 suspicious activity reports (SARs) in 2024, a 28 % increase over the previous year, while reducing manual review hours by 55 %.

Hanoi‑Based FinTech Startup MomoPay – Credit Scoring for Under‑Banked

• Collaboration: Joint venture with the State Bank of Vietnam to pilot an AI credit model for micro‑loans.
• Impact: Default rate dropped to 1.2 % (vs.3.8 % customary micro‑loan portfolios) and loan disbursement volume grew to US$45 million in the first year.

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Challenges Hindering Full AI Integration

1. Data Privacy & sovereignty

• Vietnam’s Cybersecurity Law (2022) mandates that personal data of Vietnamese citizens be stored locally. Banks must invest in secure data‑centres or partner with compliant cloud providers, adding to implementation costs.

2. Legacy System Compatibility

• Over 70 % of Vietnamese banks still run core banking platforms from the early 2000s. Integrating AI APIs requires middleware layers, increasing project complexity and risk of system downtime.

3. Talent Shortage

• The 2024 Vietnam AI Talent Survey indicates a deficit of 5,000 qualified AI engineers for the financial sector. Competitive salaries and limited local training programs make recruitment a bottleneck.

4. Regulatory Uncertainty

• While SBV’s AI guidelines provide a framework, specific rules for algorithmic transparency and liability are still evolving. Banks must adopt flexible governance structures to adapt to future regulations.

5. Customer Trust & Adoption

• A 2024 consumer poll showed 38 % of Vietnamese bank customers were hesitant to rely on AI for critical decisions (e.g.,loan approval). Clear communication about AI benefits and robust privacy safeguards are essential to build confidence.

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Emerging Trends Shaping the Future Landscape

• Generative AI for Personalized Banking – Early pilots using large language models (LLMs) to draft customized financial plans and investment insights are expected to expand in 2025.
• Edge AI for Real‑Time Payments – Deployment of AI models on local edge devices (e.g., POS terminals) enables instant fraud scoring without latency.
• Open Banking APIs with AI Middleware – SBV’s 2025 open‑banking roadmap encourages banks to expose AI‑enhanced services (risk analytics, credit recommendation) to third‑party developers, fostering an ecosystem of innovative fintech solutions.
• Sustainable Finance & AI – AI algorithms are being used to assess ESG risk for corporate borrowers, aligning with Vietnam’s 2025 green‑finance targets.

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Actionable Checklist for Bank Executives

• Conduct a AI readiness assessment covering data quality, technology stack, and talent inventory.
• Draft a risk‑based AI policy aligned with SBV’s 2023 guidelines (include model explainability, bias mitigation, and audit trails).
• Prioritize rapid‑win projects (chatbot rollout, RPA for KYC) to demonstrate ROI within 6-12 months.
• Secure strategic partnerships with accredited AI vendors or academic research labs to accelerate innovation.
• Establish a continuous monitoring dashboard for AI model performance,compliance metrics,and customer satisfaction.

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