Special Topic: 2025 INCLUSION·Bund Summit: Reshaping Innovation and Growth
"How do we replace our database?" With the arrival of the AI era, this has become the first major challenge for many Chief Information Officers (CIOs) of small and medium-sized banks upon taking office. Compared to large banks with substantial financial resources and technical reserves, small and medium banks are relatively disadvantaged in terms of customer base and capital strength, making digital transformation both an opportunity and a source of pressure.
On September 11, during the "2025·Bund Summit inclusion," the "Financial CIO Digital Intelligence Migration Closed-door Meeting" brought together CIOs from over 20 small and medium banks nationwide to exchange their concerns and explorations, with the venue completely packed. At the meeting, the "Research and Outlook on Databases for Small and Medium Banks in the AI Era" (hereinafter referred to as "the Report") was released for the first time, focusing on database iteration challenges in the digital transformation of small and medium banks in the AI era.
From manual processing to the PC era, then to networking and data centralization, banking business systems have undergone several major transformations and have now fully entered the AI era. "Throughout this process, large banks, with their substantial financial strength and technical accumulation, have achieved relatively smooth digital transformation, reaching over 90% digitalization. However, small and medium banks have fallen significantly behind in this round of digital transformation, with only about 50% digitalization, which is closely related to database replacement issues," said Gao Feng, Senior Expert at China Financial Media Group and former Chief Information Officer of the China Banking Association, at the event.
Seventy Percent of Small and Medium Banks Face Database Transformation Challenges
The Report indicates that 70% of surveyed banks stated that their existing databases cannot support the processing, analysis, and application needs of semi-structured, unstructured, and vector data, which is an important driver for building or upgrading databases and creating data foundation capabilities. Among these, 43.48% of surveyed banks consider this problem "quite important," while 26.09% consider it "very important."
Behind this massive transformation challenge lies a microcosm of the banking industry's 80-20 effect. According to 2024 banking industry analysis data, state-owned banks and joint-stock banks continue to dominate the market with 80% of revenue and profit share, with resources further concentrating toward top-tier institutions, significantly squeezing the survival space of small and medium banks. Compared to small and medium banks, large banks have obvious advantages in technology investment scale, marginal benefits, and talent reserves, often leveraging their strong technical foundation to achieve more efficient business empowerment and cost control.
However, as banks' intelligence levels deepen, the demand for massive, real-time, multi-modal data processing surges, posing severe challenges to traditional database systems in terms of scalability, implementation, and AI-native support. This is particularly crucial for resource-constrained small and medium banks in achieving "intelligent" transformation.
"Small and medium banks face numerous challenges, especially after the explosion of large models in recent years. Based on 'maximizing business needs satisfaction at minimum cost,' small and medium banks mainly face three major challenges: critical business responsibility, real-time data analysis, and AI application implementation," Gao Feng believes. However, small and medium banks also have advantages, such as relatively simple business structures, short decision-making chains, and low transformation resistance. They should leverage these advantages to choose databases that align with their positioning and characteristics, rather than pursuing comprehensive business models.
Yang Bing, CEO of OceanBase's Ocean Database, believes that on one hand, bank customer needs are undergoing profound changes, no longer satisfied with traditional standardized, channel-fragmented financial services, but expecting 24/7, omni-channel, personalized, and rapidly responsive experiences. On the other hand, peer competition forces banks to accelerate the enhancement of core competitiveness. As databases are banks' core assets, their performance and functionality directly impact business efficiency and functional experience.
Among the numerous challenges in bank database transformation, most banks believe attention should be paid to business continuity, which is crucial for customer trust. "One of the core objectives of upgrading critical business system databases in banks is to ensure 24/7 uninterrupted business operations. Even in cases of system failures or external attacks, switching can be completed in extremely short time, guaranteeing data integrity and consistency, achieving zero data loss," a rural commercial bank CIO stated.
The Report also points out that 69.57% of surveyed banks currently urgently need database disaster recovery and data consistency assurance capabilities. Additionally, 43.48% of surveyed banks express urgent need for database scalability capabilities to quickly add nodes during business peak periods such as "Double 11" shopping festivals, easily handling surges in transaction volume and ensuring customer transactions remain unaffected.
Operational Complexity and Cost Indicators Most Concerning
For small and medium banks, selecting a database that fits their positioning is a key task for CIOs and their teams. So what kind of database is suitable for small and medium banks? In fact, when choosing databases, small and medium banks particularly focus on the following factors: operational complexity, cost indicators, vendor service capabilities, domestic support, and security.
The Report shows that in the database selection evaluation process, 100% of surveyed banks consider operational complexity and cost indicators as key focus areas, while 91.30% of surveyed banks regard vendor service capabilities, domestic support, and security as important considerations.
"Small and medium banks generally face characteristics such as limited customer scale, low transaction peaks, small technical teams, and tight budgets. The core logic of their database construction is: meeting core business requirements at minimum cost while avoiding over-construction," Yang Bing said.
The Report indicates that specifically, small and medium banks should follow three major principles in database selection: First, prioritize cost-effectiveness and avoid high costs. Small and medium banks should prioritize high cost-performance database products, avoiding increased operational burden due to high costs. While meeting core business needs, choose economical solutions to ensure maximum benefits within limited budgets.
Second, reject complexity in architecture, focusing on lightweight stability. Given the smaller technical team size of small and medium banks, database architecture design should avoid complexity and choose lightweight, stable architectures. This can reduce operational difficulty, minimize potential risks from complex architectures, and ensure stable system operation.
Third, use automation to reduce labor costs in operations. With limited technical team resources, small and medium banks should fully utilize automation tools and technologies to reduce operational labor costs. Through automated operations, operational efficiency can be improved, human errors reduced, and efficient system operation ensured.
The Report also forecasts development trends for small and medium bank databases in the AI era: In the future, integrated databases will become the core development trend; AI and databases will deeply integrate and complement each other, with quantum computing potentially helping databases overcome computational bottlenecks. Additionally, domestic databases are expected to cultivate enterprises with global competitiveness.
"The market landscape will be basically determined in five years," Gao Feng said. First, domestic databases are expected to achieve comprehensive replacement of foreign databases within the next 3-5 years; second, state-owned banks are relatively smooth in database construction, but city commercial banks and rural commercial banks still face significant challenges; third, distributed databases will become the future development trend, but in critical business areas, small and medium banks are more inclined to choose integrated databases.