Recently, the GienTech Agent Innovation Competition concluded successfully, bringing together dozens of outstanding intelligent solution proposals across areas such as financial service enablement, business transformation, marketing growth, risk control, data analysis, and operational efficiency. Against this backdrop, GienTech introduced Reg@Risk, a trade banking foreign exchange risk identification agent designed to address the growing demand for stronger risk identification capabilities as foreign exchange business reform continues to deepen.

Inudstry Challenge
At present, most banks rely on expert-based scoring models for foreign exchange risk identification. While these models are compliant and controllable, they often generate excessive false alerts, require significant manual effort, and are slow to iterate. A small number of institutions have begun adopting AI-based scoring models, which offer stronger reasoning and generalization capabilities, but still face practical challenges in explainability and controllability. In response, GienTech has developed a dual-track approach that combines expert experience and artificial intelligence, balancing regulatory and audit requirements with improved risk identification efficiency.

Ontology Based Foundation

Reg@Risk uses an ontology-based methodology as a bridge, transforming complex foreign exchange risk control knowledge into a standardized language system that machines can understand, reason over, and execute. This enables the model to interpret banking terminology, risk indicators, and business relationships across data with greater precision, reducing semantic deviation and improving the consistency and reliability of its conclusions.

Practical Application

In practical application, the solution integrates multidimensional data across identity, account, and transaction domains. It supports the full process from feature recognition and risk reasoning to alert judgment and conclusion generation, while automatically producing structured risk reports supported by visual outputs such as fund flow charts and relationship network graphs. This helps shift personnel from actively investigating risks to reviewing and validating assessment results, improving the efficiency of risk analysis and shortening model iteration cycles.

Overall, Reg@Risk not only enhances the breadth, depth, and efficiency of foreign exchange risk identification, but also provides banks with a new pathway to building a more explainable, more efficient, and more adaptable intelligent risk control system.

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