Evaluating the Effectiveness of Money Laundering through ESG Risk in the Indian Banking and Financial System

Abstract

Money Laundering (ML) poses a significant threat to the integrity of the global financial system, with increasing concerns about its linkages to Environmental, Social, and Governance (ESG) risks. This study explores the largely overlooked intersection between ESG compliance and ML risks within India’s financial sector and offers actionable strategies for strong governance and sustainable growth.
Adopting a mixed-methods research framework, the study integrates Quantitative analysis conducted using SPSS tool to assess the correlation between ESG scores and ML risks, while qualitative insights are derived through thematic analysis using Thematic analysis.
Additionally, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to identify key relationships among ESG factors influencing ML activities. The findings reveal that ESG factors do play a crucial role in either facilitating or mitigating ML risks. The study also highlights a significant gap in awareness among financial institutions regarding the intersection of ESG risks and ML. Despite regulatory advancements, many financial institutions lack robust policies to integrate ESG considerations into ML frameworks.
The findings indicate that poor governance structures, inadequate social accountability, and minimal environmental oversight collectively increase exposure to money laundering. Despite increasing regulatory focus on both ESG and ML compliance, a majority of financial institutions in India have yet to recognize the synergy between the two topics. ESG is still being treated as a reporting obligation rather than a strategic tool for risk management. To address these challenges, the study recommends integrating ESG-based metrics into ML risk assessments, enforcing stricter regulatory standards, and developing training programs to enhance awareness. Furthermore, the adoption of the latest technologies like AI and machine learning tools, is suggested for real-time monitoring and pattern detection.
This research advocates for a paradigm shift, where sustainability and financial integrity are not parallel concerns but interconnected goals. Aligning ESG compliance with ML strategies can not only reduce systemic vulnerabilities but also position Indian banks and financial institutions as leaders in ethical and responsible banking. In an era marked by increasing scrutiny and stakeholder activism, the cost of inaction is too high, making immediate and informed response a strategic imperative.