Environmental-Anomaly Index for Early Detection of Green-Money-Laundering Risks in Climate-Finance Portfolios

Abstract

This study proposes an Environmental-Anomaly Index (EAI) to identify inconsistencies between environmental performance and financial flows in climate-finance projects. The index is computed from three components: satellite-observed environmental deviations, inconsistencies in ESG reporting, and discrepancies between risk ratings and capital-allocation patterns. A dataset of 2,036 green-finance projects from 14 institutions between 2016–2022 was used for evaluation. Incorporating EAI features increased model AUC from 0.79 to 0.88 and precision at 60% recall from 0.43 to 0.64. Projects in the t op-risk decile contained 71.3% of known problematic cases. Back-testing showed that applying EAI-based screening could have reduced misallocated capital by an estimated 28.1%. The results demonstrate the potential of environmental anomaly monitoring for early detection of green-money-laundering risks.