The article examines design methodologies for constructing AI-native growth platforms in niche financial institutions. The relevance of the study is determined by the rapid transition of financial services toward unified, real-time, and model-centric architectures that overcome the limitations of legacy multi-engine systems. The novelty lies in presenting an integrated analytical synthesis of architectural, operational, and governance principles that jointly define AI-native scalability. The work describes the structural transformation of data pipelines, analyzes constraints of SQL, NoSQL, and NewSQL systems, and studies decisioning, fraud detection, customer intelligence, and regulatory automation workflows. Special attention is given to the role of embedded MLOps and distributed intelligence in sustaining continuous learning. The study aims to identify methodological foundations enabling small institutions to achieve enterprise-grade analytical performance. Comparative analysis, source evaluation, and conceptual generalization are applied. The conclusion outlines an integrated model of AI-native growth and defines practical implications for banks, regulators, and technology designers.

AI-native architecture, financial institutions, data pipelines, real-time analytics, decision intelligence, fraud detection.

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