Traditional relational database–based search approaches often suffer from performance degradation, high query latency, and limited capability in handling large-scale textual and semantic data. These limitations become more pronounced as the volume and complexity of banking transactions grow.

This study proposes the design and implementation of a secure, scalable, and distributed hybrid architecture within the Tejarat Bank transaction ecosystem. The proposed system aims to balance security, efficiency, and intelligent data processing. The architecture begins by separating sensitive and non-sensitive data to reduce exposure risks. Sensitive information is protected through encryption mechanisms and secure storage strategies. To improve retrieval capabilities, a semantic analysis layer based on semantic indexing is introduced, enabling concept-based search instead of traditional keyword-based retrieval.

In addition, a distributed architecture is employed in which storage, indexing, and query processing are deployed across multiple independent nodes. This design enhances system availability, fault tolerance, and horizontal scalability while distributing computational workload efficiently. A reactive data integration layer connects structured transaction data with semantic indexes using unique identifiers, ensuring consistent and real-time interaction between components.

Experimental evaluation demonstrates that the proposed architecture significantly improves both system performance and retrieval accuracy compared to a conventional Oracle database-based approach. In a pilot implementation involving one million transaction records, the average query response time was reduced from 10,000 milliseconds to 2,000 milliseconds. Furthermore, the distributed design improved system throughput and scalability under high request loads.