AUTOMATION OF CLIENT INTERFACE INTEGRATION INTO BANKING AND FINTECH ECOSYSTEMS USING LARGE LANGUAGE MODELS

In the contemporary digital environment, banks and fintech institutions face the need to ensure continuous and efficient interaction among diverse systems and service channels, which has become a critical factor of competitiveness.Automation of customer interface integration not only increases service speed but also optimizes operational processes and improves the user experience by reducing manual request handling.The use of large language models (LLMs) opens new opportunities for banking and fintech institutions, as these models are capable of processing large volumes of data, generating natural language, and adapting responses to individual user needs, thereby enhancing the efficiency and quality of service delivery.Recent studies in this field confirm the substantial potential of large language models for transforming banking services.O. Skakalina and A. Holub demonstrate that the use of large language models in customer service, marketing, internal operations, and product development can increase productivity, reduce costs, and stimulate innovation, while also requiring careful attention to data quality control, security, and ethical considerations [1].In turn, the study by M. Ali, C.-M. Leong, H. P. Koh, S. A. Raza, and C.-H. Puah shows that the integration of AI technologies such as ChatGPT is positively perceived by users, and that their intention to use these services is largely determined by the degree of task-technology fit and the level of awareness of the technology's functionality [2].Despite these evident advantages, previous research has paid insufficient attention to the comprehensive evaluation of architectural solutions for integrating large language models across multiple interaction channels, to the alignment of contextual information between systems, and to ensuring process continuity and security in high-stakes environments like high-frequency trading (HFT) and institutional risk management, leaving critically important aspects of practical implementation unresolved.Drawing from over 8 years of hands-on leadership in fintech, including directing client integrations and AI enhancements for Nasdaq's cloud-native Risk Platform (NRP) in high-frequency trading environments, the author brings practical insights into applying advanced technologies for institutional-scale systems.This experienceencompassing FIX Protocol optimizations, Kafka-based real-time analytics, and AI reductions in onboarding times by 30% for multimillion-dollar contracts-highlights