AI Reshapes the Competitive Logic of the Banking Sector

As global AI technology continues to evolve at an accelerated pace, the financial sector—a highly data- and scenario-intensive industry—has become a core battleground for AI application.

Shengma Finance learned that from the APEC CEO Summit China, the Lujiazui Forum to the annual general meetings of several listed banks, core management teams in the banking sector have been publicly disclosing their AI strategic roadmaps, progress in scenario implementation, and medium- to long-term planning, marking a wave of concentrated industry strategic positioning.

Behind this collective voice, industry consensus and diverging paths have become increasingly clear. Gaps in implementation pace, technology path selection, and commercial value realization capabilities among different types of banks continue to widen, signaling that the banking sector's AI competition has officially moved from conceptual piloting into a phase of differentiated acceleration.

Tiered Differentiation Takes Shape: Three Types of Banks Pursue Differentiated Paths

Following early-stage technology accumulation and scenario piloting, a clear tiered structure has emerged in the AI-driven transformation of China's banking sector. Banks of varying sizes and positions have adopted distinct implementation strategies based on their inherent strengths.

State-owned major banks consistently prioritize compliance, security, and financial stability, characterized by comprehensive coverage and steady progress.

Liu Jun, President of the Industrial and Commercial Bank of China (ICBC), stated at the 2026 APEC CEO Summit China that financial institutions must be fully prepared for the AI era, emphasizing that technology must ultimately serve practical applications. For systemically important financial institutions, safety must be a top priority when using AI, while simultaneously promoting deep alignment of human resources, data, and financial assets.

Gu Shu, Chairman of the Agricultural Bank of China (ABC), emphasized at the Lujiazui Forum and shareholder meetings that AI should serve as a business support tool rather than a job replacement. The human resources freed up by intelligent automation will be redirected to areas such as due diligence and offline post-loan visits, which AI cannot yet cover. ABC has established a tiered control mechanism to match differentiated AI technology solutions according to scenario risk levels.

In terms of implementation data, ICBC's "ICBC IntelliFlow" large model has deployed over 500 AI applications across multiple business areas, with AI digital employees handling workloads equivalent to 55,000 person-years annually. China Construction Bank's (CCB) AI assistant achieves 99.42% coverage in responding to branch counter inquiries, with over 100,000 daily visits.

Joint-stock banks, in contrast, focus more on commercial value validation, leveraging Racetranization (sector-specific) and product-based strategies to gain market advantage. For instance, China Merchants Bank (CMB) has implemented an "AI First" strategy, deploying 183 specialized models across various fields and launching China's first credit card with embedded Token computing power benefits, directly integrating AI capabilities into retail customer acquisition. Industrial Bank has proposed that AI is evolving from a "assistant" to a "business-driven engine," with over 200 AI agents deployed internally, viewing agent technology as a definitive future growth direction. Compared to the steady approach of state-owned major banks, joint-stock banks are more inclined to reconstruct job boundaries and organizational processes through AI, exploring quantifiable input-output models.

Internet-based private banks focus on micro and small enterprise inclusion scenarios, adopting lightweight and sunken ( sunken can be translated as "grassroots-oriented" or " lower-tier markets -focused") AI service models. Leveraging technological advantages, they lower the threshold for financial services by embedding AI capabilities into core inclusive finance areas such as credit approval and customer service, creating a differentiated competitive track from traditional banks.

Uneven Implementation: The "Last Mile" Challenge in Value Realization

Despite sustained AI investment, implementation shows significant structural disparities. While auxiliary scenarios have rapidly gained traction, breakthroughs in core business areas remain slow. The challenge of converting technological investment into commercial value persists as a common industry hurdle.

Surveys indicate that current AI applications in the banking sector are highly concentrated in low-risk, repetitive auxiliary scenarios. Intelligent customer service, internal Q&A, document processing, and report generation exhibit the highest penetration rates, with some banks already handling over 80% of routine customer inquiries via AI and achieving over 80% efficiency improvements in intelligent financial statement recognition. These scenarios, characterized by high technological maturity and low compliance risks, enable rapid cost reduction and efficiency gains, making them priority areas for bank implementation.

In contrast, deep AI integration in core business areas such as credit approval, asset pricing, and fund trading faces multiple obstacles. On one hand, financial scenarios demand high explainability and accuracy, creating inherent conflicts between large models' "hallucination" issues and black-box characteristics versus financial risk control requirements. Most banks still adopt an "AI-assisted + manual final review" model, retaining human decision-making authority for core decisions. On the other hand, uneven underlying data governance capabilities and complex legacy system architectures also constrain end-to-end implementation of AI models across core business processes.

The industry's value realization gap remains significant. Boston Consulting Group's 2025 global survey revealed that just over half of financial institutions reported realizing value from AI projects, with only 3% achieving large-scale commercial value realization, highlighting a vast gap between leading and lagging institutions.

The domestic market exhibits similar characteristics: leading banks have begun establishing mature AI cost-benefit accounting systems, while many small and medium-sized banks remain in technology selection and piloting stages, with AI investments yet to generate scaling ( scaling can be translated as "large-scale" or " scaling 回报" as " scaling returns") returns. As the industry shifts from "concept hype" to "ROI-driven precision," technological disparities among banks are rapidly translating into operational gaps.

The Intensive vocalization ( Intensive vocalization can be translated as "frequent public statements") from senior executives signal that the banking sector's AI transformation has moved beyond blind follow-the-leader phases into a rational, results-oriented implementation race. Safety and compliance remain non-negotiable baselines, human-machine collaboration is a long-term certainty, and commercial value serves as the core metric for implementation success.

AI is reshaping the competitive logic of the banking sector, with core competitiveness shifting from traditional assets and branch networks to data capabilities, algorithmic sophistication, and tech talent density. For all banks, the finish line of this race is not technology itself but whether it can genuinely translate into service capabilities and operational efficiency.

END

Follow us for more insightful content.

Original content by Shengma Finance. Unauthorized reproduction prohibited.