Against the backdrop of artificial intelligence rapidly reshaping the financial industry landscape, Binah Capital Group Inc (BCG) recently released "Banking Transformation 2035: The Paradigm Shift Towards the Agent Bank." Authored by BCG Global Senior Partner He Dayong, Global Partner Tan Yan, and BCG Global Think Tank Advisor Sun Zhongdong, the book focuses on the strategic direction, organizational reshaping, and implementation pathways for AI transformation in banking, introducing for the first time the concept of the "Agent Bank."

The Banking Industry Advances Towards the Bank 4.0 Era

"Banking Transformation 2035" points out that the banking sector is undergoing a critical transition from digitalization to intelligence. Competitive advantages previously built on branch network coverage and regulatory barriers are continuously weakening, with banking services gradually evolving into "fluid algorithms."

Currently, the Chinese banking industry commonly faces challenges such as "low interest rates, low net interest margins, high risks, and intense internal competition," making traditional growth models unsustainable. Simultaneously, the rapid maturation of large language models and Agent technology is creating new development opportunities for the sector.

The publication proposes that commercial banking has already progressed through three developmental stages: Bank 1.0, Bank 2.0, and Bank 3.0, evolving from addressing the "availability" of financial services, to enhancing "convenience," and then to strengthening "connection depth." The forthcoming Bank 4.0 era—the "Agent Bank"—will further realize the intelligence and autonomy of banking services. Future banks will be composed of a multitude of agents possessing perception, memory, planning, and execution capabilities, achieving a shift from "passive response" to "proactive service."

BCG Managing Director and Global Senior Partner He Dayong stated, "AI is reshaping the underlying operational logic of the banking industry. The future competition among banks will not only be about technological capability but also about organizational capability and the ability to reconstruct scenarios."

A Systematic Methodology for Building the Agent Bank

Confronted with the profound changes brought by AI, the banking industry urgently needs to move from technological pilots to systematic transformation. To this end, BCG proposes a comprehensive "CJR + V.R.F. + ADC" methodology framework.

CJR (Customer Journey Reshaping): Reconstructs service processes around genuine customer needs, eliminating low-value steps.

V.R.F. (Value-Risk-Feasibility Adaptive Framework): Balances business value, risk governance, and technical feasibility to achieve both innovation and robust development.

ADC (Agent Design Card): Translates strategic objectives into executable agent design standards, clarifying responsibilities, capabilities, trigger mechanisms, and risk contingency plans.

BCG Global Think Tank Senior Advisor Sun Zhongdong emphasized, "Blindly adopting large models will only create chaos; what banks need is a rigorous methodology. CJR points the direction, V.R.F. provides navigation, and ADC manages implementation."

AI Agents Accelerate Penetration into Core Business Scenarios

"Banking Transformation 2035" systematically reviews 45 core business scenarios in banking, covering areas such as transaction banking, corporate finance, retail finance, inclusive finance, and data technology.

In cross-border finance, an intelligent document review Agent can achieve automatic identification of letter of credit risks, significantly improving document processing efficiency; a global liquidity management Agent enables real-time fund transfers across time zones and currencies.

In corporate finance, a comprehensive due diligence Agent can integrate multi-dimensional data including business registration, judicial, tax, and public sentiment, increasing corporate due diligence efficiency severalfold; a business data credit Agent, by connecting to real-time enterprise operational data, enables more precise credit decision-making.

In retail finance, a wealth management Agent can dynamically adjust service strategies based on client behavior and needs; an AI digital companion Agent can upgrade traditional mobile banking from a "functional portal" to an "intent portal," delivering more proactive and personalized customer service.

Simultaneously, in back- and middle-office areas such as data governance, regulatory reporting, and IT operations, agents are also driving banks to build more efficient and agile "digital nervous systems."

Human-Machine Collaboration Reshapes Banking Organizational Structures

The book posits that the AI era will not only change technological systems but also reshape the talent structure and organizational models of banks.

BCG Managing Director and Global Partner Tan Yan noted, "Future bank organizations will gradually evolve from traditional pyramid structures to 'pine tree' structures: the scale of entry-level positions will contract, middle-level professional capabilities will strengthen, senior management will focus on strategic decision-making and resource allocation, with overall talent density continuously increasing."

Concurrently, banks need to focus on cultivating three key types of talent:

AI Navigators: Responsible for formulating AI strategy and technological direction.

AI Designers: Possess both business understanding and technical capability, driving AI product design and implementation.

Change Experts: Responsible for organizational process reengineering and transformation facilitation.

From L1 to L5: The Evolution Roadmap for the Agent Bank

"Banking Transformation 2035" further proposes a five-level maturity model for the Agent Bank, outlining the industry's evolution path for the next decade.

L1 Information-Enhanced Assistant: Provides knowledge retrieval and information support.

L2 Task-Assisting Co-pilot: Embeds into business processes to assist decision-making.

L3 Domain-Autonomous Commander: Possesses independent planning and execution capabilities.

L4 Cross-Domain Collaborative Agent: Achieves cross-departmental, multi-scenario collaboration.

L5 Ecosystem-Symbiotic Agent Mesh: Connects the bank with external ecosystems, enabling autonomous collaboration and value creation.

As banks' intelligence levels continuously improve, the human-machine relationship will also gradually evolve from a model of "human-led, AI-assisted" to a new paradigm of "human-machine collaboration and co-intelligence in decision-making."

He Dayong concluded, "The bank of 2035 will not be defined by today's dreamers, but by the practitioners who start taking action now. Faced with the profound transformation brought by AI, the greatest risk is not failure after trying, but hesitation and stagnation in the face of change. Only by acting early can one seize the initiative in future competition."