[Manho Yoon's Financial Focus] Transforming the "Brain," Not the "Physical"

Published 27 Apr.2026 11:10(KST)

Global Finance Enters the Era of AI Agents

Beyond Automation to "Intelligent Decision-Making"

McKinsey Report: "Value Creation of 510 Trillion Won Annually"

Redesigning the Financial Industry Beyond Mere Technology Adoption

[Manho Yoon's Financial Focus] Transforming the "Brain," Not the "Physical"

The recent surge of artificial intelligence (AI) across global industries is manifesting in distinctly different survival strategies, depending on the core nature of each sector. While manufacturing and retail are focusing on 'physical AI' by combining robots and automation equipment, the financial industry—where intangible data and advanced decision-making are key—is evolving on an entirely different level. The sector is now entering the era of 'AI agents' that act on behalf of human judgment and execution.

The Evolution of Financial AI

Initially, the financial sector employed AI primarily for so-called Robotic Process Automation (RPA), which automated repetitive administrative work. However, the recent trend has moved beyond simple rule-based automation. AI is now advancing to the stage of 'intelligent decision support,' where it can interpret data autonomously and present optimal alternatives tailored to various situations.

The most striking sign of this transformation is found in internal control and compliance. Previously, abnormal signs were detected after the fact by reviewing countless transaction records. Now, intelligent AI agents are capable of learning vast amounts of laws and internal guidelines, reviewing documents in real-time, and logically inferring potential violations. Rather than merely flagging data anomalies, AI can offer the reasoning behind its judgments and suggest the direction for action. As a result, the structure of work itself is being redesigned, allowing humans to focus on strategic approvals and responsibility based on intelligent insights produced by AI.

Expanding Application in Financial Work

The role of AI is extending beyond internal efficiency to encompass areas such as wealth management (WM) and investment banking (IB). In wealth management, AI agents are being reborn as real-time intelligent guides. AI instantly analyzes global news and market indicators to assess their potential impact on client portfolios. This enables AI to propose optimized response strategies to each client in a timely manner, supporting both asset protection and opportunity capture simultaneously.

AI as an Essential Survival Strategy

The Importance of Data and Infrastructure Systems

For financial institutions, the starting point for AI lies in the data. Not only must structured data be managed, but also unstructured data—such as documents, voice, and images—needs to be refined and organized so AI can utilize it effectively. Since various documents and records are core assets in finance, establishing a foundation for utilizing such unstructured data is becoming even more crucial.

Infrastructure also requires transformation. Generative AI and agent-based services demand large-scale computing power, making it essential to secure a GPU-based computing environment capable of stable processing. At the same time, given the need for agility in financial data, infrastructure must be built to meet stringent security and control requirements.

Integration with legacy systems is another critical factor. AI agents in finance are not about creating entirely new work, but rather about improving and restructuring existing work processes. Therefore, AI operating independently without linkage to existing systems is unlikely to yield meaningful results. It is vital to design AI and core work systems so they are seamlessly connected and can operate within actual workflows.

AI-Centric Re-design of Financial Operations

To achieve successful AI transformation, it is necessary to go beyond simply adopting AI technology and instead redesign the financial business itself with AI at its core. Recently, Woori Bank identified 175 use cases for AI agents and began a company-wide process redesign. This is a promising endeavor, as it represents a declaration to restructure not just specific departments, but also the entire spectrum of banking operations—including internal controls, wealth management, corporate lending, work automation, workforce, and organizational systems—around AI.

In the future, financial institutions should consistently pursue 'AI innovation (AX)' by expanding AI adoption in line with their future strategies and enhancing their survival competitiveness. This includes remodeling business models around AI, transitioning to HR innovation and organizational cultures that enable joint decision-making with AI, and establishing governance frameworks to control new risks arising from expanded AI application.