CNN-Based Image Validation for ESG Reporting: An Explainable AI and Blockchain Approach

Abstract

With sustainability increasingly becoming a major focal point in FinTech innovations, Green FinTech platforms have been forced to justify the authenticity of their environmental claims. Yet, the existing auditing processes, which are overwhelmingly dominated by manual inspections and document-based verifications, have proved insufficient in guaranteeing transparency, speed, or scale. The matter is more acute when the data to be validated constitutes visual or geospatial evidence: images of solar installations, satellite visuals of deforestation, or drone footage of carbon offset projects. To bridge that gap, this present study attempts to establish an AI-driven visual auditing framework that exploits the deep learning technique-aided by convolutional neural networks (CNNs)-to perform automatic image-based compliance monitoring for ESG-aligned FinTech platforms. The framework can ingest multi-source visual inputs, including satellite imagery and drone views, alongside on-site IoT camera feeds and investor-submitted images, to assess the environmental compliance of a project remotely and almost instantaneously. Domain-specific datasets train deep learning models to capture environmental indicators, which include the placement of renewable infrastructure, illegal land clearing, water contamination, and fraudulent waste disposal. Performance analysis illustrates that the classification accuracy of over 92% and precision rate surpassing 90% can be achieved by the CNN-based system when it is used for the identification of non-compliant activities. In order to make the decision boundaries of the model interpretable-a key requirement for enhancing transparency to regulators and stakeholders-XAI methods, including SHAP and Grad-CAM, are integrated into the system. For audit-result integrity and traceability, outputs of audits are recorded in a permissioned blockchain using smart contracts that automatically deliver alerts and reports. Regulatory integration is supported through the standardization of the output format of the system in line with major reference frameworks, such as the Task Force on Climate-related Financial Disclosures (TCFD) and Sustainable Finance Disclosure Regulation (SFDR). This paper introduces a novel, scalable, and policy-aware framework that fills the gap between AI-enabled automation and regulatory compliance in sustainability auditing. It also tackles the ethical dimension from all vantage points by embedding transparency and explainability in every layer of the system architecture. The findings suggest that AI-powered visual auditing has the potential to disrupt the traditional methods whereby FinTech firms validate and report on environmental performance, thus combating greenwashing and sustaining trust within sustainable finance ecosystems.