SHANGHAI, September 22, 2026 — At HUAWEI CONNECT 2026, Sunline showcased its latest collaboration with Huawei in financial data governance, demonstrating how AI can be embedded across the data management lifecycle to help banks establish a trusted, governed and AI-ready data foundation.
As banks move from isolated AI use cases toward enterprise-wide adoption, the quality, consistency, security and accessibility of underlying data are becoming increasingly critical. AI agents and intelligent applications require more than access to large volumes of data; they depend on governed data that can be understood, trusted and used consistently across business and technology environments. Against this backdrop, Sunline and Huawei have jointly developed an integrated data management solution designed for the AI era, combining Huawei's underlying technology infrastructure with Sunline's banking-domain expertise, data capabilities and implementation experience.
The solution was featured at Huawei's global finance showcase during HUAWEI CONNECT 2026, alongside broader discussions around the evolution toward Agentic Banking.
Building an AI-Ready Data Foundation
The joint solution adopts a four-layer architecture spanning infrastructure, technology, applications and portal services, connecting the underlying computing environment with data management capabilities and business-facing applications.
Rather than applying AI as an additional layer on top of conventional data governance processes, the architecture embeds AI capabilities throughout the data management lifecycle. This enables banks to progressively automate and enhance key governance activities while improving the quality and usability of the data required by analytics, AI models and intelligent agents.
The data governance component focuses on four fundamental areas:
- Metadata management
- Data quality management
- Data security governance
- Data standards management
Together, these capabilities are designed to strengthen the controls, consistency and operational efficiency required to turn enterprise data into trusted data assets for AI-driven banking.
AI-Assisted Metadata Management
Metadata remains fundamental to understanding how data is defined, structured, sourced and used across a bank. The solution introduces a closed-loop operating model combining AI-assisted metadata completion with human validation. AI automates repetitive metadata enrichment while domain specialists remain responsible for review and confirmation.
Under this approach, the time required to complete metadata for an individual field can be reduced from approximately three to five minutes to around 12 seconds, representing an efficiency improvement of more than 93%. By reducing the manual workload associated with large-scale metadata maintenance, banks can move metadata governance from periodic, resource-intensive exercises toward a more continuous operating model.
Automating Data Quality Management
For banks operating across complex products, systems and regulatory environments, translating business requirements into scalable data-quality controls can be resource-intensive. The solution combines visual configuration, drag-and-drop rule design and AI-assisted recommendations to convert complex banking requirements into standardised data validation rules.
This can increase the automation rate of data-quality checks to approximately 95%. AI is also applied to quality reporting. The time required to produce a data-quality report can be reduced from approximately five person-days to 0.5 person-day, while data-set completeness can reach 99%. The objective is not simply to detect data issues faster, but to establish a more systematic mechanism for maintaining data that is accurate, complete and fit for downstream analytics and AI consumption.
Strengthening Data Security Governance
As financial institutions expand the use of enterprise data across AI and digital applications, identifying and controlling sensitive data becomes increasingly important. The solution embeds financial-industry data governance and compliance requirements into the governance process and applies AI agents to assist with sensitive-data discovery, classification and categorisation.
Automated classification accuracy can exceed 90%, helping banks identify sensitive information at scale and apply appropriate governance controls throughout the data lifecycle. This provides a stronger foundation for balancing broader data utilisation with security, privacy and regulatory requirements.
Establishing Consistent Enterprise Data Standards
AI systems are only as reliable as the data and definitions behind them.
Inconsistent terminology, duplicate definitions and fragmented data standards can create different interpretations of the same customer, product, transaction or business metric across an organisation.
Sunline's data governance capabilities cover both foundational financial data standards and business metric standards, supporting lifecycle management from standard definition and implementation through revision and version control.
By establishing consistent definitions across systems and business domains, banks can reduce data ambiguity, improve interoperability and create a more reliable foundation for enterprise-wide data sharing, analytics and AI applications.
From Data Governance to Agentic Banking
The role of data governance is changing as banking moves deeper into the AI era. Traditional governance programmes were largely designed to ensure that data could be controlled, reported and consumed by people and enterprise applications. Agentic Banking introduces another requirement: data and knowledge must increasingly be structured so that AI systems and agents can interpret, retrieve and act on them reliably.
This shifts the role of data governance from primarily managing data assets toward establishing the trusted data and knowledge foundation for enterprise intelligence.
For Sunline, the collaboration with Huawei brings together complementary capabilities across financial infrastructure, data platforms, banking-domain knowledge and solution delivery. The two companies have expanded their collaboration from the China market into international financial-services markets, supporting banks at different stages of technology modernisation and AI adoption.
As AI becomes more deeply embedded into banking operations, Sunline will continue working with Huawei to advance the integration of financial data, AI and banking technology, helping institutions establish the governed data foundation required to move from AI experimentation toward scalable, production-grade intelligent banking.
