Satellite Data and Artificial Intelligence for FINtech

Highlights: What are the main findings? The study presents SAIFIN (Satellite data and Artificial Intelligence for FINtech), a modular multi-agent trading framework that fuses OHLC market data, news sentiment, and satellite-derived indicators. By introducing specialized Market, News, Satellite and Master Agents coordinated through LLM-based orchestration, the system aims to produce coherent, explainable recommendations, with high agreement between quantitative signals and generated narratives. What is the implication of the main finding? The results indicate that multimodal, agent-based architectures that combine generative AI and alternative data (including satellite-derived environmental indicators) offer a practical route to more robust and transparent algorithmic trading systems, particularly under volatile or data-sparse market conditions. The SAIFIN framework provides a reusable blueprint for regulation-aware decision-support platforms in finance, showing how explainability, heterogeneous data integration, and high-performance computing can be jointly engineered to meet modern requirements on accuracy, latency, and interpretability. The SAIFIN project (Satellite data and Artificial Intelligence for FINtech) develops a novel algorithmic trading system that integrates satellite imagery, financial data, and advanced artificial intelligence to enhance decision-making, particularly in commodity and agricultural markets. This paper presents the motivation, design, implementation, and validation of the SAIFIN framework. Leveraging alternative data and modular multi-agent architectures, SAIFIN aims to deliver robust, context-aware trading signals in diverse market conditions.