Following the global trend, prominent banks in Vietnam have invested in researching and implementing AI technologies in their operations.
For instance, TPBank has integrated face recognition technology into its LiveBank automatic banking channel, bolstering security and convenience for customers. VietinBank utilises kiosks with FaceID recognition to identify customers and forward their requests to advisors, as well as serve as valuable assistants.
Other banks like VietABank, Nam A Bank, VPBank, Techcombank, VIB, and ACB have embraced AI across various functions, including chatbots for customer support and engagement, asset management, security, fraud prevention, and analysis of peak season ATM withdrawals.
The incorporation of AI technology in the banking sector not only optimises operational costs but also enhances customer support and enables efficient process automation. AI has proven highly advantageous for revolutionising data management, customer behaviour understanding, and fostering robust customer relationships.
However, it is essential to note that most banks in Vietnam employ traditional rule-based AI, which excels in handling routine enquiries and assisting with simple financial transactions. This type of AI can only automate tasks that have been programmed into it, and its training is usually tailored for specific stationary tasks, making it less adaptable to new situations or tasks.
In contrast, generative AI possesses the ability to be trained on a wide range of data and can adapt to various situations and changes, but its application in the banking sector remains limited.
Generative AI stands as a next-generation technology that takes automation to a higher level by empowering computers to generate fresh content and ideas, moving beyond mere data processing and analysis.
The significant difference between traditional AI and generative AI is their learning and adaptive capabilities. Generative AI can process past data, learn from it, and make intelligent decisions based on this knowledge, while traditional AI is confined to performing tasks designed for it.
Generative AI can continually re-train, update, and adjust its predictions, diagnoses, and decisions in response to new data inputs. This adaptability aligns with the increasing demand for personalised financial services driven by customer preferences.
Furthermore, generative AI can access the information necessary to undertake complex tasks related to customer information, and complete simple or complex automated payments as an autonomous AI agent without human supervision.
