Page 489 - ISC PROCEEDINGS 21.4

Customer Service: Hyper-personalization of experiences at scale; (2) Operational
Management and Automation: Optimizing performance through RPA and AI; (3)
Wealth Management and Investment: Democratizing premium financial services for all
audiences; (4) Risk Management and Security: Enhancing defense capabilities against
high-tech crimes; (5) Ecosystem and Open Banking: Seamless connectivity between
financial and non-financial sectors.
In particular, within the context of Vietnam as a dynamic transitioning economy,
this research examines the practical outcomes of AI implementation at pioneering banks
such as TPBank, MB, Techcombank, VIB, and VPBank. By doing so, the paper not only
elucidates the values of business efficiency but also emphasizes the philosophy of 'Digital
Humanism'—a vision where cutting-edge technology must remain human-centric to
sustain trust and empathy in the era of machines.
2. Literature review, theoretical framework, and research methodology
2.1. Overview of related research
Several scholars have conducted significant research on this topic, providing not
only academic contributions but also laying the groundwork for AI governance
frameworks in global finance:
Major Research Streams on Banking in the AI Era Globally
International scholars have approached this topic through three primary lenses:
business model transformation, technical tools, and intelligent automation.
Regarding the transformation of the nature of transactions and the "Invisible Bank"
model, author Tony Boobier (2020) confirms the inevitable shift of "money" from physical
form to digital data. This author proposes five core pillars, emphasizing the "Invisible
Bank" concept—where customer experience is given the ultimate priority and physical
branches gradually transition their functions.
In contrast to the strategic approach, within the data-driven technical approach to
finance, Yves Hilpisch (2020) focuses on practical implementation using the Python
programming language. This work marks a paradigm shift from "Model-driven Finance" to
"Data-driven Finance" , utilizing machine learning algorithms—such as Random Forests
and Support Vector Machines (SVM)—and deep learning architectures, including
Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to forecast
market fluctuations.
Regarding the Intelligent Automation (IA) approach , Pascal Bornet (2020) clarifies
the intersection between AI and RPA (Robotics) , creating 'Digital Labor' that assists in the
instantaneous processing of customer requests and the optimization of loan appraisal
processes.
Multidimensional Approaches and Algorithmic Ethics Trends
Recent studies, notably by Marc Lazo and Ryan Ebardo (2023), have expanded the
scope of evaluation from three perspectives: banks, customers, and regulatory bodies.
Strategic Weapon: AI is considered a key tool for banks to counter the penetration of
Fintech companies. Barriers and Ethics: The research highlights major concerns regarding
data privacy and algorithmic transparency. These findings form the basis for the
emergence of the "Hyper-personalization" trend and the urgent requirement for fair
algorithms to maintain public trust.
Research Practice in Vietnam
In the domestic market, studies typically focus on the status of digital
transformation at pioneering commercial banks such as TPBank, VPBank, and

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