Uncovering the Key Drivers of Microfinance Repayment Challenges: A Regional Study of Microfinance Operations in India

Repayment default remains a key challenge in the field of microfinance, threatening the sustainability of lending models designed to support low-income borrowers. This study examines the intricacies and potential impact of the problem by collaborating with a mid-sized Indian microfinance company to investigate the underlying root causes of borrower default in India. Through structured and detailed interviews with three Regional Heads (RHs) and fifteen Relationship Managers (RMs) responsible for borrowers in Bangalore, Maharashtra, and Tamil Nadu (India), the study captures field-level insights into the financial and behavioral factors that contribute to missed payments. Furthermore, to evaluate the potential impact of artificial intelligence in replicating human expertise, the study compares these field insights with simulated responses generated by four popular large language models (LLMs): ChatGPT-4o, Claude 3.7 Sonnet, Perplexity, and Llama 4. The comparison reveals that while LLMs successfully identify many of the key drivers of default reported by the RH/RMs, they fall short in capturing certain context-specific nuances. The findings contribute to the broader debate on whether AI will complement or substitute human roles, particularly in sectors requiring localized knowledge, and interpersonal understanding.