AI for Classifying Renewable Energy Assets through Image Recognition in Suitable Fintech Platform

Abstract

The integration of Artificial Intelligence (AI) with financial technology (FinTech) provides transformative opportunities for sustainable investment and renewable energy financing. Recent advancements in image recognition and deep learning enable automated classification of renewable energy assets, such as solar panels, wind turbines, and hydropower facilities, thereby enhancing transparency and verification in sustainable finance. This study proposes an AI-driven framework that leverages convolutional neural networks (CNNs) for asset classification and links verified renewable infrastructure with FinTech platforms for investment, pricing, and risk assessment. By embedding explainable AI mechanisms within decision support systems, the proposed model reduces greenwashing risks and ensures traceability of sustainable investments. The integration further supports dynamic pricing and portfolio optimization in digital marketplaces, while enabling scalability through blockchain-enabled smart contracts. This interdisciplinary approach addresses gaps in conventional sustainable finance models by providing real-time monitoring and automated asset validation. The contribution lies in combining AI-based image recognition with FinTech innovation to create a reliable, transparent, and scalable mechanism for channeling capital into verified renewable energy projects. The framework is expected to strengthen investor confidence, align with circular economic principles, and accelerate the global transition toward green finance.