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For decades, the competitive advantage of traditional banking has been grounded in capital scale, operating networks, and customer information. With the rise of Open Banking, however, data has begun to move out of banks. The advantage does not disappear; it shifts to those who can exploit data more effectively.
Open Banking is often described as banks allowing third parties to access customers’ account data via APIs, with customer consent. Yet the core issue is broader than the technical mechanism. Open Banking represents an institutional reform that reallocates control of financial data from traditional intermediation institutions to a wider ecosystem that includes fintechs, digital platforms, and technology companies.
In this ecosystem, data is no longer “locked” within banks. It becomes a stream of big data. Personal financial data functions as a reusable asset with near-zero marginal cost, while also helping reduce information asymmetry. As access expands, competitive advantage shifts from “owning data” to “the ability to exploit data.”
A key paradox is that opening data does not automatically disperse power. In many cases, power concentrates faster among actors with stronger data analytics capabilities. If Open Banking is not carefully designed, it can weaken traditional banks while strengthening large technology platforms.
Open Banking can deliver significant economic benefits, but these outcomes depend on institutional design:
These benefits may fail to materialize if data is opened without common standards, leading to fragmentation. They may also be undermined if protection mechanisms are weak, eroding user trust. If analytical capacity is concentrated among a few actors, the market may become less competitive rather than more.
The risks of Open Banking often run deeper than data leakage. One concern is “over-interpretation,” where the consent mechanism can be more formal than substantive. Users may not read or fully understand terms but still consent to share data, creating an “illusion of control” while real control rests with the data-using party.
A more advanced risk is inference. From financial transaction data, systems can deduce income, spending habits, and even sensitive personal characteristics. In such cases, the privacy boundary shifts from the data itself to what can be inferred from it.
Another systemic issue is algorithmic bias. If AI models are trained on historical data, they may reproduce or amplify existing inequalities. In credit decisions, this can result in some customer groups being evaluated unfavorably without clear remedies.
As data moves across banks, fintechs, and intermediary platforms, assigning responsibility when incidents occur becomes more complex. Supervisory costs can rise, and legal gaps may emerge.
Countries do not adopt the same model because each reflects different trade-offs among efficiency, innovation, and privacy:
These three models reflect different balances among efficiency, innovation, and privacy.
For Vietnam, the challenge is not to copy a single model but to design an approach suited to domestic institutional conditions. Since Open Banking is still at an early stage and the personal data framework is still being refined, a hybrid model may be prudent: open data with controls, encourage innovation while maintaining oversight, and allow experimentation within a clearly defined framework.
More broadly, Open Banking should be treated as part of a national data strategy rather than only a banking-sector initiative. When data becomes a core economic resource, data governance becomes national governance over power in the digital economy.
[Prof. Truong Quang Thong - School of Business, University of Economics Ho Chi Minh City]
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