Toward Sustainable AI in Digital Financial Services: A Hybrid Data-Grounded Architecture for Reducing Generative Model Dependency

AI assistants are increasingly integrated into digital financial services; however, relying on generative language models for response generation can introduce challenges related to response reliability, data exposure, and resource requirements. This study presents a hybrid data-grounded architecture that selectively uses generative AI while maintaining reliable natural language interaction. The proposed task-oriented system combines semantic intent matching, slot and entity extraction, reconciliation, and response routing. Instead of using the generative model to describe every response, the architecture constructs template-based responses directly from structured institutional data whenever sufficient information is available and invokes the generative language model only as a fallback. The architecture was evaluated using a fixed set of 100 realistic user queries, including diverse expressions and spelling variations, across 14 iterative system refinements while the underlying AI models remained unchanged. Response correctness was manually assessed against the underlying data and classified as correct, unanswered, or incorrect. Correct responses increased from 51% to 97%, whereas incorrect responses decreased from 33% to 0%. Simultaneously, language-model-generated responses decreased from 34% to 10%, whereas data-grounded deterministic responses increased from 66% to 90%. These findings indicate that system-level architectural refinement can substantially improve response reliability while reducing dependence on generative response production. However, the observed reduction in generative-model dependency should be interpreted as a potential resource-conscious architectural strategy rather than evidence of a measured environmental benefit.