How a Chinese Bank Used AI Large Models to Revolutionize Data Development

China Everbright Bank’s data‑asset team identified three systemic pain points in the financial industry’s data development: (1) heterogeneous platform silos that force separate development tracks, (2) a proliferation of discrete tools that break workflow continuity, and (3) fragmented data‑link quality that hampers reliability. These issues stem from the coexistence of legacy MPP databases and newer Hadoop‑based components, which isolate data, methods, and tools.

Regulatory guidance from the China Banking and Insurance Regulatory Commission and the Ministry of Industry and Information Technology emphasizes the need for multi‑stack management, autonomous controllability, and low‑code/visual development platforms. In response, Everbright designed a large‑model‑powered integration solution.

Solution Overview

The solution restructures data development into a unified, intelligent workflow with four key capabilities:

Automated Break‑Point Elimination : By consolidating the requirement‑management system, metadata platform, task scheduler, and code repository, the bank creates a closed‑loop “automatic de‑breakpoint” process that monitors each transformation step, improving pre‑emptive risk control and overall efficiency.

Lake‑Warehouse Integrated Development : A self‑built platform abstracts over heterogeneous sources (e.g., Hive, GaussDB) to provide deep compatibility, masking underlying differences and standardising development patterns. This reduces the technical adaptation burden and lets developers focus on core data logic.

Intelligent Data‑Link Layer : Large‑model AI augments the platform with capabilities such as natural‑language interaction for lake‑warehouse tasks, automatic explanation of ETL scripts, long‑SQL and slow‑SQL analysis, and intelligent quality checks for deployment packages.

Code‑Quality Baseline : An intelligent assistance suite embeds code templates, custom static analysis, an enriched knowledge base, and prompt‑engineering to detect defects and normalize quality across developers, mitigating skill‑level variance.

Implementation has already covered core scenarios like source‑data ingestion and change management. The platform’s stability and practicality have been validated, and three concrete benefits are projected:

Reduced development cycles by collapsing multiple role‑specific steps through automated de‑breakpoint, accelerating data‑access changes.

Lowered difficulty of handling heterogeneous data sources via deep compatibility, cutting adaptation effort.

Higher defect detection rates in the development chain, decreasing incident frequency and boosting overall quality.

Beyond immediate efficiency gains, the solution shifts data development from a fragmented, manual, and reactive model to an integrated, AI‑assisted, and proactive paradigm. The underlying intelligent framework and quality‑inspection mechanisms become reusable assets for future enterprise‑level platforms, opening new avenues for full‑lifecycle data‑asset management and strengthening the conversion of data elements into business value.

Future work will follow a phased roadmap, extending the blueprint across functional layers while maintaining a balance of quality and security, and continuously leveraging AI large‑model capabilities to refine the intelligent data‑development ecosystem.