Small and medium-sized enterprises (SMEs) have limited financial resources, so they rely heavily on labor to sustain operations. This explains why they tend to remain small, struggle to scale, and find it difficult to grow into large enterprises. External support is also limited and insufficient to drive growth, leaving many enterprises fragmented over long periods.
Regarding SME access to credit, five key constraints can be identified. The first is collateral requirements, which are almost mandatory in traditional lending. SMEs typically lack high-value assets, often renting premises and using low-value equipment, putting them at a disadvantage compared to larger firms.
The second is limited transparency. Large enterprises usually have well-established accounting systems, audited financial statements, and strong credibility with banks. In contrast, many SMEs lack structured governance, and some even lack proper accounting functions. Incomplete financial data and limited track records make it difficult for banks to assess performance and risk.
The third is difficulty in proving cash flow. In the absence of collateral, banks may lend based on cash flow, but SMEs often lack clear input-output contracts and stable revenue streams, making any credit assessment challenging.
The fourth is higher risk. Due to limited management capacity, many SMEs operate with one person handling multiple roles, without professional risk control systems. This increases perceived risk and makes lenders more cautious.
The fifth is cost efficiency for banks. The process of appraisal, monitoring, and debt recovery for small loans is nearly the same as for large loans, while returns are significantly lower. This reduces banks’ appetite for SME lending under traditional models.
It is also important to note that banks must ensure the safety of the financial system. Lending capital largely comes from public deposits, so capital preservation is critical. International standards such as Basel require strict risk controls, making it difficult to relax credit conditions. These “one-size-fits-all” criteria - collateral, transparency, and risk management - unintentionally favor large enterprises and disadvantage SMEs.
Improving access to finance requires adjustments on both sides. Banks need more flexible lending approaches tailored to SMEs, while these businesses must enhance governance, improve transparency, and standardize operations.
In the long term, a shift from collateral-based lending to data- and cash flow-based lending is inevitable. With fintech support, banks can leverage diverse data sources, such as transactions, taxes, and supply chains, to assess businesses more comprehensively. This allows more flexible financing, even at the level of individual transactions, rather than large, complex loans. However, SMEs must also accelerate digital transformation to ensure data transparency and connectivity.
Credit guarantee funds are another important tool. They act as intermediaries to reduce risk for banks and enable firms without collateral to access funding. In principle, these funds commit to repayment in case of default, strengthening lender confidence.
However, their effectiveness remains below expectations. Limited capital restricts their ability to provide guarantees, and their administrative, non-market-oriented operations reduce flexibility and responsiveness.
Therefore, financial capacity must be strengthened, operational mechanisms modernized, and funding sources diversified, mobilizing private capital and applying tools such as re-guarantees and risk-sharing. Only then can guarantee funds become effective leverage for SME financing.
Ultimately, improving SME credit access requires a coordinated approach: reforming lending methods, accelerating digital transformation, strengthening legal frameworks, and enhancing support mechanisms.
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While the banking sector once focused on computerization, the current priority is comprehensive digitalization. At a higher level, “digital-creating banking” goes beyond digitizing operations to generate new value by connecting with other sectors to form ecosystems. This evolution can be seen in three stages: traditional banking, digital banking, and digital-creating banking.
Vietnam’s banking system is still transitioning toward this model, but progress is uneven. Differences exist in technology platforms, awareness, service capability, and value creation.
The legal and policy framework is still evolving, aiming to establish common standards aligned with market development. Open banking is a key example, requiring institutions to share data with partners and third parties to develop services. In this context, banks have proactively developed their own standards and solutions, highlighting the need for greater coordination and standardization, especially in technology and security.
Assessing which models can truly “create value” in Vietnam remains challenging due to incomplete and inconsistent data. As a result, banks’ transformation capacity depends largely on internal capabilities, particularly technology platforms and implementation capacity.
In reality, many banks still operate on legacy core banking systems deployed years ago, requiring structured upgrade roadmaps. Beyond technology, the complexity of setting priorities and implementation strategies also contributes to slow and uneven transformation.
The “leapfrogging” approach in digital transformation can shorten development timelines but carries risks if key learning stages are skipped, potentially leading to system design gaps and weaker risk control.
While it is widely acknowledged that data is foundational and requires a comprehensive governance strategy, such statements are often too general. South Korea’s MyData model offers a more practical approach, organizing data ecosystems around specific use cases with clearly defined roles and responsibilities from the outset.
In this model, data sharing is based on mandatory licensing, standardized APIs (Application Programming Interfaces), and explicit user consent. Roles, such as data subjects, providers, MyData operators, processors, and third parties, are clearly defined and may vary depending on use cases. Data Protection Impact Assessments (DPIAs) and Data Processing Agreements (DPAs) are applied to each data flow to manage risks and clarify accountability. This approach shows that data governance is no longer a broad strategic concept but an operational capability tied directly to products, partners, and specific use cases.
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The main bottlenecks in small and medium-sized enterprises (SMEs)’ access to credit lie in three areas. First, collateral: SMEs, especially in manufacturing, have assets but these are often in the form of leased facilities, machinery, inventory, orders, or cash flows. While economically-valuable, they do not meet banks’ traditional preference for tangible, legally-clear collateral. The issue is not a lack of assets, but how they are recognized and valued.
Second, financial transparency. Many SMEs, often family-run, maintain accounting systems primarily for tax purposes rather than for governance or lending requirements. This creates a gap between bank data standards and what SMEs can realistically provide, complicating credit appraisal.
Third, risk assessment. SMEs are highly flexible, with fast capital turnover, but credit evaluations still rely heavily on static financial statements and historical data. This can underestimate their operational efficiency and business viability.
Overall, while credit standards are sound in principle, they are not fully suited to SME realities, helping explain persistent financing constraints. In addition, risk perception plays a role. Despite similar processing costs to large corporate loans, banks remain more cautious toward SMEs due to perceived higher risk, even though data does not necessarily show higher non-performing loan rates.
While SMEs are often seen as slow to adapt, both banks and businesses need to change - though banks, with greater resources and technological capacity, are better placed to lead and should take a more proactive role.
At the same time, SMEs must improve governance, transparency, and financial standards. While many are making progress, transformation remains costly and slow, particularly for small, family-run firms with limited systems and resources.
As a result, SME reform cannot rely on individual effort alone. A coordinated support ecosystem, spanning digital tools, data infrastructure, and compliance support, is essential to enable meaningful and sustainable change.
SME support funds have fallen short of expectations due to limited resources, weak coordination with banks, complex procedures, and low trust among stakeholders. As a result, the main bottleneck is not funding, but the lack of an effective operating mechanism, highlighting the need for meaningful reform.
Improving SME access to finance requires shifting from collateral-based lending to cash flow and data-driven assessment, alongside legal reforms to better align with SME realities. A coordinated support ecosystem, centered on shared data, and more effective credit guarantee mechanisms are also essential. Equally important is a shift toward data-based risk evaluation, which can better reflect SMEs’ strengths and unlock both greater access to capital and new growth opportunities for the banking system.
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In recent years, digital technology’s advances have driven transformation in the banking sector. In this context, “digital-creating banking” represents a more advanced stage than digital banking. While digital banking focuses on digitizing products and optimizing customer experience, this model expands into a more comprehensive role.
Banks not only provide services but actively support business development by building digital ecosystems, leveraging data, and offering decision-making tools.
With big data systems and integrated technology platforms, banks can help businesses access market insights, customers, partners, and investment opportunities more effectively, enhancing service quality, reducing risk, and promoting growth.
Structurally, this model rests on three key platforms. First, the banking platform acts as a central hub connecting services and data, enabling businesses to access a full suite of tools beyond credit and payments, including market and customer intelligence. Second, open banking combined with APIs (Application Programming Interfaces) allows data sharing between banks, businesses, and fintech firms. Third, integrated applications (super apps) and embedded finance enable financial services to be seamlessly integrated into business platforms, improving user experience and reducing costs.
However, implementing this model in Vietnam faces several challenges. Technologically, it requires advanced infrastructure and strict standards for data security and safety, which are not yet consistently applied.
Human capital is another barrier. Banks need personnel who understand both technology and finance and can act as advisors to businesses, requiring a shift in mindset and organizational culture.
The legal framework also lags behind. Issues such as data sharing, privacy protection, and the boundary between “support” and “intervention” remain unclear, slowing cooperation and creating legal risks.
For businesses, this model offers tangible benefits. Access to finance improves through data-based assessment rather than reliance on collateral. Continuous, multi-dimensional data enables more accurate evaluations.
Businesses also benefit from data ecosystems and networks, helping them find partners, expand collaboration, and improve efficiency. Banks can support cash flow management, optimize investment portfolios, and guide long-term strategies.
However, participation requires certain conditions. Many small and medium-sized enterprises (SMEs) lack the resources, governance capacity, and technological readiness needed. Since the model depends on data and system connectivity, enterprises must have digital infrastructure, digitized data, and the ability to share information.
Applying all these standards at once may create barriers. A more flexible approach, starting with pilot groups and scaling gradually, is needed.
As Vietnam advances digital transformation, developing a policy framework for this model is essential. This includes controlled sandbox mechanisms, digital talent development, infrastructure investment, and common standards for connectivity.
The State should play a facilitating role by building shared infrastructure and enabling businesses, especially micro and small enterprises, to integrate into digital ecosystems.
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In practice, the concepts of “digital banking” and “digital-creating banking” are not just different terms, they reflect two fundamentally different stages of banking system development. Digital banking, in its common understanding, involves applying technology to digitize traditional products, services, and operational processes. In this model, banks primarily act as financial service providers, offering credit, payments, and digital utilities to individuals and businesses.
By contrast, digital-creating banking represents a structural leap forward. Banks move beyond service provision to deeply leveraging data and technology to build open platforms. On these platforms, businesses, partners, and fintech firms can connect, interact, and participate in a shared ecosystem. Banks thus become an integral part of the value chain, directly contributing to value creation for businesses rather than merely supporting from the outside. Vietnam is currently at the transition point from digital banking to digital-creating banking.
Some financial institutions have begun developing “platform banking” models, integrating banking systems with enterprise management systems such as ERP (Enterprise Resource Planning) via APIs (Application Programming Interfaces). However, the distinction between the two models lies not in technology but in the extent of the bank’s involvement in core business activities, especially in providing data and supporting decision-making. Deeper involvement can generate significant value, but also introduces risks, particularly as the boundary between “support” and “intervention” becomes blurred.
To manage these risks, several foundational principles must be established. First, data ownership must remain with the enterprise. Banks can provide infrastructure, tools, and analytics but should not replace decision-making. Second, transparency in data use must be ensured, including scope of sharing, purpose, and accountability in case of incidents. Third, security must be a prerequisite, as even a minor vulnerability in a connected ecosystem can lead to widespread risks.
From a technology enterprise perspective, the biggest challenge today is not technology itself, but trust. Both banks and businesses remain cautious about sharing data due to concerns over security and legal liability, limiting the depth of integration and data utilization. Technical standardization, especially for APIs, is another significant bottleneck. The lack of common standards means each bank develops its own system, forcing businesses to invest heavily in separate integrations, which is particularly burdensome for small and medium-sized enterprises (SMEs).
In addition, the cybersecurity capacity of many organizations has not kept pace with ecosystem expansion, leading to caution or even delays in adopting new models. Businesses must also strengthen their own data governance, cybersecurity capabilities, and compliance with information security standards. This is essential to building mutual trust; the core of any data-driven collaboration model.
From a legal standpoint, completing the regulatory framework is urgent. Clear rules on data sharing, ownership, and responsibilities must be established to build trust within the ecosystem.
At the same time, common standards for Open APIs are needed to ensure consistency and scalability, with coordination led by regulators rather than fragmented development by individual institutions. Third-party risk management must also be strengthened through monitoring and security assessment mechanisms.
Finally, cybersecurity and data protection must be central. Organizations should implement real-time monitoring systems to detect and respond to incidents promptly, and adopt a “security by design” approach. Retrofitting security after deployment is both inefficient and costly, and introduces significant restructuring risks.
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Stricter tax authority management of input and output invoices, along with the rollout of e-invoicing and tighter control over invoice trading, has significantly improved transparency among small and medium-sized enterprises (SMEs).
Previously, the existence of multiple accounting systems reduced financial reliability, while oversight focused mainly on output invoices, making it difficult to detect discrepancies in inputs.
With improved transparency, banks’ appraisal costs have declined, creating conditions to expand credit to SMEs. In this context, fintech platforms act as intermediaries, providing data infrastructure that helps businesses declare, account, and comply more effectively, while optimizing operating costs. For banks, this provides structured data, improving credit assessment, shortening appraisal processes, and creating room to lower lending rates.
Through accounting software, all documents, legal records, and contracts are digitized and centrally stored. Businesses retain full control over data sharing, and can provide complete datasets or selective access based on bank requirements. However, selective sharing may increase time costs due to the volume of documents.
At the platform level, the system acts as a digital data repository, enabling storage and connectivity of all financial and accounting information. With business consent, data can be automatically shared with relevant parties, reducing manual processes. Thanks to connectivity and standardization, banks can easily access data and even move toward automated appraisal processes, including AI applications.
For example, with inventory data, the system can instantly identify origin, invoices, and payment status, enhancing reliability when used as a basis for credit decisions. For businesses, digitizing documents combined with outsourced accounting services simplifies operations, allowing them to focus resources on core business activities.
