The rapid adoption of FinTech solutions in Sri Lanka, particularly Digital Wallets and Buy-Now-Pay-Later (BNPL) services, has significantly reshaped the financial services sector. Despite this growth, little is known about how users perceive these technologies. This study employs Natural Language Processing (NLP) techniques to analyze user-generated reviews from the Google Play Store, aiming to assess customer sentiment toward seven widely used Digital Wallets and two BNPL platforms in Sri Lanka. Sentiment classification was performed using the following approaches: lexicon-based methods, namely TextBlob and Valence Aware Dictionary and sEntiment Reasoner (VADER); machine learning models, including Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), and Random Forest; and the deep learning model Bidirectional Encoder Representations from Transformers (BERT). All these models were assessed against the manually annotated ground truth labels, and among them, SVM achieved the highest accuracy of 89.7% for Digital Wallet reviews, while BERT outperformed all other models on BNPL reviews with an accuracy of 85.3%. These models were subsequently used to analyze sentiment proportions, extract commonly used positive terms, and identify trends in sentiment over time. The findings reveal consistently positive sentiment toward Digital Wallets, whereas BNPL services showed a shift from early negative sentiment to increasingly positive engagement, reflecting changes in consumer experience and acceptance over time.