ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Finance
User Perceptions of RBI-Approved P2P Digital Lending Apps: An NLP, Machine and Deep Learning Approach
Provisionally accepted
- VIT-AP University, Amaravati, India
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ABSTRACT: Digital lending, often known as alternative lending, refers to fintech platforms that offer quick and easy loans via digital channels, bypassing many of the limits of traditional banking. Digital lending has become a prominent Fintech innovation since the mid-2000s, with rapid growth in India due to financial inclusion measures, but it continues to face difficulties such as fraud, transparency, and consumer unhappiness. This study's main objective was to find out how consumers see and assess Indi's RBI approved P2P Digital lending apps by mining massive amount of customer feedback to identify strength, shortcomings, and overall satisfaction levels. Research analysed a final dataset of 15408 user reviews collected from seven RBI-Approved digital lending platforms of 5Paisa, Faircent, i2iFunding, LenDenClub, CahKumar, Lendbox, and IndiaMoneyMart, derived from an initial 15537. The reviews were preprocessed with text cleaning, tokenization, and sentiment tagging before being examined with NLP, machine learning, and deep learning techniques. Topic modeling revealed 11 recurring topics, Sentiment analysis revealed that 55% of evaluations were good, 41% were negative, and 4% were neutral, with strengths in loan disbursement, withdrawals, and EMI payments, but flaws in interface design, rejection transparency, and login functioning. Comparative data showed that IndiaMoneyMart and i2iFunding had the highest user perception, while 5Paisa and Lendbox trailed owing to continuous complaints regarding transparency, accessibility, and user experience. In terms of modeling, the deep learning model VGG16 and ensemble machine learning techniques (XGBoost, CatBoost, and LightGBM) consistently produced the best predicted accuracy (up to 0.88), surpassing more straightforward models like Decision Tree or ResNet. Overall, the results show how digital lending can promote financial inclusion, but they also emphasize how better user interface/user experience (UI/UX), rejection transparency, and customer service are necessary to build confidence and encourage long-term adoption. Keywords: Digital lending, RBI Approved apps, Fintech, User perception, Sentiment analysis.
Keywords: Digital lending, RBI Approved apps, FinTech, User perception, sentiment analysis
Received: 18 Sep 2025; Accepted: 26 Nov 2025.
Copyright: © 2025 Raja Sekhar and Saheb. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shaiku Shahida Saheb
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