🤖 AI Summary
This work addresses the fragmentation of existing AI systems in finance, which typically operate in isolation and struggle to jointly handle tasks such as robo-advising, high-frequency trading, dynamic recommendation, strategic banking interactions, and cross-modal sentiment analysis. To overcome this limitation, we propose the first unified multimodal intelligent finance framework that integrates proximal policy optimization (PPO)-based reinforcement learning, time-series forecasting, in-context learning, Nash equilibrium computation from game theory, and cross-modal embeddings. We further design an ensemble optimization method with convergence guarantees. Extensive experiments demonstrate that our framework achieves significant improvements: a 23.7% gain in portfolio optimization performance, a 31.2% reduction in high-frequency trading error, an 18.9% increase in investment recommendation accuracy, a 27.4% acceleration in game-theoretic convergence speed, and a 15.6% boost in sentiment analysis accuracy—collectively unlocking the synergistic potential of multifaceted AI techniques in finance.
📝 Abstract
The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously. This paper presents a groundbreaking unified framework that seamlessly integrates Proximal Policy Optimization for robo-advisory systems, advanced time-series prediction models for high-frequency trading, in-context learning mechanisms for dynamic investment advisory, game-theoretic approaches for competitive banking scenarios, and unified embeddings for cross-modal financial sentiment analysis. Our comprehensive framework addresses the critical gap in existing literature where these technologies have been developed in isolation, failing to leverage their synergistic potential. Through extensive experimentation across multiple financial datasets and real-world scenarios, we demonstrate that our integrated approach achieves superior performance compared to specialized single-domain systems. Specifically, our framework shows a 23.7% improvement in portfolio optimization metrics, reduces prediction error in high-frequency trading by 31.2%, enhances investment recommendation accuracy by 18.9%, optimizes competitive banking strategies with a 27.4% increase in Nash equilibrium convergence speed, and improves sentiment analysis accuracy by 15.6% through cross-modal fusion. The theoretical foundation of our work establishes convergence guarantees for the integrated optimization problem, while our empirical results validate the practical applicability across diverse financial institutions. This research not only advances the state-of-the-art in financial AI but also provides a blueprint for developing comprehensive intelligent systems that can adapt to the complex, interconnected nature of modern financial markets.