This article systematizes the engineering challenges encountered by the author when scaling fintech systems from inception to millions of users, including ensuring ultra-low latency, transactional consistency, and high availability. As a solution, a comprehensive framework is proposed, based on orchestrating containerized applications using Kubernetes, applying high-performance data handling patterns such as Command Query Responsibility Segregation (CQRS), and analyzing the architectural implications of integrating artificial intelligence (AI) technologies. The aim of the study is to systematize engineering challenges related to scaling FinTech platforms and to propose a framework of architectural patterns and cloud-oriented solutions to overcome them. The scientific novelty of the work lies in synthesizing fundamental distributed systems theory, modern cloud-native development practices, and forward-looking analysis of AI’s architectural impact. This synthesis forms a comprehensive guide bridging the gap between academic theory and applied engineering practice, offering a strategic approach to building next-generation FinTech systems. The methodological basis of this work is founded on a synthetic approach that combines several research methods to form an analysis of the problem of scaling FinTech systems. The work demonstrates the practical significance of the proposed model, derived from the author’s applied experience, for building fault-tolerant and high-performance financial systems that withstand extreme loads and comply with strict industry requirements.
Andrii Humeniuk (2025) studied this question.