Customer satisfaction in peer-to-peer lending platforms: A text mining and sentiment analysis approach

This study examines customer experience and satisfaction with peer-to-peer (P2P) lending platforms in India by analyzing user-generated online reviews. Despite the rapid expansion of India’s P2P lending market, few studies have analyzed consumer feedback to evaluate platform performance. To address this gap, 11,000 customer reviews were scraped from nine leading Indian P2P platforms. Text mining and sentiment analysis techniques, specifically Frequency Analysis, Convergence of Iterated Correlations (CONCOR) cluster analysis, and Exploratory Factor Analysis (EFA) were employed to extract latent satisfaction drivers. The analysis identified key experience drivers such as customer support, loan processing speed, usability, and fraud-related concerns. EFA distilled these into three underlying satisfaction factors: Positive Experiences and Core Functionalities, Customer Support and Overall Experience, and Efficiency in Application Interaction. The study reveals India-specific insights into digital lending behavior and provides targeted recommendations for improving platform trust, responsiveness, and financial accessibility, essential to user retention and financial inclusion in India’s evolving FinTech ecosystem.