Page 498 - ISC PROCEEDINGS 21.4

Challenges Core Solutions Expected Outcomes
Deploying Multi-factor Biometrics Absolute security and
Deepfake Risks
and AI-driven security layers. trust for customers.
Adopting Microservices A flexible system for
Cumbersome Legacy Architecture and API-first seamless integration of
Systems
strategies. emerging AI.
Source: Compiled by the author from bank reports
Data and technological solutions
Financial institutions are aggressively pivoting toward a 'Cloud-first' strategy,
transitioning their infrastructure from on-premise servers to cloud computing
environments. This migration empowers banks to perform large-scale data processing
with real-time latency. Simultaneously, banks are prioritizing the construction of holistic
customer data profiles by centralizing information from disparate departments—
including cards, credit, insurance, and deposits—into a unified repository. By eliminating
'data silos,' this integration provides AI with a comprehensive and objective perspective,
thereby mitigating errors in credit approval processes
Human resources and cultural solutions
Financial institutions are implementing a dual strategy of internal capacity building
and external talent acquisition to secure a workforce proficient in both financial
operations and AI technology. Leading pioneers—including MB, VPBank, and TPBank—
have established dedicated internal training academies to foster a data-driven mindset
and equip employees with AI-assisted tools. Furthermore, banks are restructuring into
cross-functional, agile squads comprising both banking specialists and data engineers.
This collaborative model facilitates a 'shared language,' ensuring that AI product designs
are highly aligned with practical requirements. Additionally, banks are sourcing elite
domestic and international talent through industry-wide competitions and strategic
recruitment initiatives.
Risk management and ethical solutions
By implementing Explainable AI (XAI), banks foster trust among customers and
regulatory bodies through enhanced transparency. Furthermore, institutions are
integrating AI with Blockchain technology to fortify data security and employing
adversarial AI to continuously stress-test their own systems, identifying vulnerabilities
before they can be exploited by cybercriminals. Simultaneously, commercial banks are
proactively collaborating with the State Bank of Vietnam (SBV) within regulatory sandbox
mechanisms to refine legal frameworks for emerging services, such as app-based lending
and digital identification.
Ecosystem and partnership solutions
Financial institutions should foster strategic partnerships with Big Tech and Fintech
entities while maintaining strict data sovereignty. By exposing Open APIs to third-party
providers, banking AI can be seamlessly embedded into non-financial ecosystems—such
as travel bookings and utility payments. This integration facilitates the acquisition of more
diverse and multifaceted data sets, enhancing the AI's predictive capabilities.
4. Conclusion
Artificial Intelligence is no longer a mere technological option; it has become an
existential necessity for the survival of the modern banking industry. This revolution has
dissolved physical boundaries, transforming cumbersome financial institutions into
intelligent, agile, and more empathetic digital entities. Notwithstanding the persistent

497