A COMPARATIVE STUDY OF AI-DRIVEN AND TRADITIONAL APPROACHES TO FINANCIAL COVENANT AND COMPLIANCE MONITORING AT MUFIN GREEN FINANCE

Abstract

This study looks at how AI-driven financial covenant and compliance monitoring stacks up against traditional methods used by Mufin Green Finance, a leading non-banking financial company in India’s green finance sector. Traditionally, covenant checks depend on manual reviews, periodic audits, and rule-based systems. These processes can be slow, require a lot of labor, and are often delayed by human error. With the rise of machine learning, natural language processing, and predictive analytics, financial institutions can now analyze borrower data in real time. They can identify anomalies early and predict risks rather than just reacting to them later. The research compares both methods based on accuracy, efficiency, scalability, and cost. It also examines practical issues like data quality, model interpretation, and regulatory acceptance. The study is part of a broader change in India’s financial services industry, where NBFCs like Mufin Green Finance are essential for improving access to credit and supporting sustainable development through electric mobility and clean energy financing. By looking at Mufin’s operations, strategic goals, and competitive environment, the project shows how AI- enabled monitoring can improve risk management, support compliance, and strengthen portfolio resilience. The findings add to the ongoing conversation about the role of AI in creating sustainable, transparent, and future-ready financial systems, particularly in emerging markets where green finance is growing quickly.