CLPS Incorporation (the “Company” or “CLPS”) (Nasdaq: CLPS) today announced the successful launch of its proprietary AI-driven intelligent migration solution. Designed to transition legacy COBOL systems to Java, this solution represents a significant advancement in the digital transformation of retail banking and core banking infrastructures.
The Evolution of COBOL in Modern Finance
Since its inception in the 1960s, COBOL (Common Business-Oriented Language) has served as the backbone of global financial transaction systems. Valued for its stability and efficient processing capabilities, COBOL continues to power critical operations for thousands of financial institutions, including account administration, payment clearing, and credit services.
However, the banking industry currently faces a “COBOL Cliff.” As the global pool of COBOL-literate programmers shrinks and maintenance costs for mainframe environments rise, institutions are increasingly hindered by their inability to integrate with modern cloud-native and AI-driven ecosystems. In contrast, Java offers a high-performance, object-oriented framework that provides the cross-platform compatibility and extensive library support essential for modern, scalable financial applications. Efficiently and safely migrating these mission-critical systems to modern stacks has become a strategic priority for the global banking sector.
Two Decades of Domain Expertise
Since 2005, CLPS has specialized in the maintenance, upgrade, and modernization of COBOL-based core banking systems. This long-standing focus has enabled the Company to develop an understanding of the complex business logic and regulatory requirements inherent in core banking ecosystem. This deep-rooted expertise positions CLPS as a preferred partner for financial institutions navigating complex technological shifts.
Synergy of Human Insight and Artificial Intelligence
In an era of AI advancement, CLPS maintains a clear philosophy: AI is a powerful force multiplier, but the core value remains centered on human expertise.
The Company firmly believes that by integrating the deep domain expertise of its senior technical experts with the high-efficiency processing capabilities of AI, it can leverage established industry foundations to achieve transformative innovation. Grounded in this strategic approach, CLPS has launched an AI-powered intelligent migration solution spearheaded by its internal expert team. Utilizing Large Language Models (LLMs), the solution employs a semantic understanding and cross-language conversion mechanism tailored specifically for financial systems. It integrates a full-cycle efficient development and verification process to achieve high-precision restoration of COBOL business logic.
Key technical features of the solution include:
- Reduced Resource Dependency: The AI-assisted framework significantly minimizes the demand on the client’s internal resources during the migration process.
- Knowledge Graph Construction: For “black-box” legacy systems lacking documentation, the team utilizes static analysis and dynamic tracing to reconstruct business rules.
Successful Proof-of-Concept and Market Outlook
CLPS recently achieved a milestone by completing a Proof-of-Concept (PoC) for a major bank in Hong Kong. The project demonstrated AI-enabled migration could rapidly and accurately convert COBOL code to Java while maintaining complete system stability and core logic integrity. This achievement not only showcases the immense potential of AI in core system modernization but also further solidifies CLPS’s leading position in this domain.
Mr. Raymond Lin, Chief Executive Officer of CLPS, stated: “This business segment represents a substantial market opportunity. By synthesizing two decades of banking domain expertise with cutting-edge AI tools, we are providing a scalable pathway for financial institutions to lower operational overhead and accelerate their digital transformation. We believe this solution will be a key growth engine for CLPS as we help our clients navigate the complexities of legacy system modernization.”
