Adaptive Financial Recommendation Systems Using Generative AI and Multimodal Data

Abstract

The intersection of generative artificial intelligence (GenAI) and financial technology (fintech) is redefining how financial services are conceptualized, delivered, and experienced. As consumer expectations shift toward hyper-personalization, traditional recommendation systems—rooted in rule-based algorithms and shallow learning paradigms—fall short in addressing the dynamic, contextual, and human-centric nature of financial decision-making. This research introduces a novel framework that harnesses the capabilities of GenAI, specifically large language models (LLMs) and multimodal learning, to generate personalized financial product recommendations based on real-time transactional data, behavioral signals, and inferred user intent. This approach fuses techniques from natural language processing, reinforcement learning, and time-series modeling to continuously learn from user interactions, adapting recommendations across life stages and financial contexts. Furthermore, the framework is designed with ethical AI principles at its core, embedding differential privacy, fairness-aware modeling, and explainability layers to ensure regulatory compliance and build user trust. We conduct a robust evaluation using synthetic yet realistic financial datasets, benchmarking against collaborative filtering, matrix factorization, and neural recommender baselines. Results show up to 30% improvement in recommendation relevance, a 25% increase in user engagement, and a notable enhancement in adaptability and interpretability metrics. The proposed GenAI-powered system sets a new direction for intelligent, responsible, and adaptive financial ecosystems in the era of open banking and AI-driven digital transformation.