Аналіз можливостей побудови гіперперсоналізованого фінансового помічника на основі користувацьких фінансових даних

Abstract

This paper explores the construction of hyper-personalized financial assistants powered by agentic artificial intelligence and user-specific data. Unlike traditional monitoring systems that lack context, these AI-driven tools leverage spending patterns and life goals to provide strategic advice and predictive analysis. The research analyzes various application models, including AI chatbots for transaction management and “financial mentors” for optimizing cashflows. Advanced features of these systems include location-based recommendations for more economical shopping. Ultimately, agentic AI represents a high-demand direction for the evolution of personal financial management applications