Design Methodologies for Building AI-native Growth Platforms in Niche Financial Institutions
Keywords:
AI-native architecture, financial institutions, data pipelines, real-time analytics, decision intelligence, fraud detection.Abstract
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.
References
Balajee, A. B. (2025). MLOps in finance: Automating compliance & fraud detection. International Journal of Computer Trends and Technology, 73(4), 35–41. https://doi.org/10.14445/22312803/IJCTT-V73I4P105
Burgos, D., Kranas, P., Jimenez-Peris, R., & Mahíllo, J. (2022). Architectural patterns for data pipelines in digital finance and insurance applications. In J. Soldatos & D. Kyriazis (Eds.), Big data and artificial intelligence in digital finance (pp. 31–52). Springer. https://doi.org/10.1007/978-3-030-94590-9_3
Eken, B., Pallewatta, S., Tran, N., Tosun, A., & Babar, M. A. (2025). A multivocal review of MLOps practices, challenges and open issues. ACM Computing Surveys, 58(2), Article 39. https://doi.org/10.1145/3747346
John, M. M., Holmström Olsson, H., & Bosch, J. (2025). An empirical guide to MLOps adoption: Framework, maturity model and taxonomy. Information and Software Technology, 107, 107725. https://doi.org/10.1016/j.infsof.2025.107725
Ogenyi, F. C., Ugwu, C. N., & Ugwu, O. P.-C. (2025). A comprehensive review of AI-native 6G: Integrating semantic communications, reconfigurable intelligent surfaces, and edge intelligence for next-generation connectivity. Frontiers in Communications and Networks, 6, 1655410. https://doi.org/10.3389/frcmn.2025.1655410
Sebastian, D. (2025). Designing resilient insights platforms: Data architecture principles for scalable decision intelligence in financial services. International Journal of Computational and Experimental Science and Engineering, 11(4), 7809–7825. https://doi.org/10.22399/ijcesen.4152
Somu, B., & Sriram, H. (2023). Next-gen banking infrastructure: Designing AI-native IT architectures for financial institutions. Journal for ReAttach Therapy and Developmental Diversities, 6, Article 3610. https://doi.org/10.53555/jrtdd.v6i10s(2).3610
Sukharevsky, A., Kerr, D., Hjartar, K., Hämäläinen, L., Bout, S., Di Leo, V., & Dagorret, G. (2025, June). Seizing the agentic AI advantage: A CEO playbook to solve the gen AI paradox and unlock scalable impact with AI agents. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage
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Copyright (c) 2026 Pradhan Anubhav
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