Sarita Vishwakarma, Anjali Shejal, Vaishnavi Kewadkar, Anjali Mane, Mrs. Snehal Patil

DOI: https://doi.org/10.5281/zenodo.24130514

The rapid growth of digital financial technologies has increased the adoption of Buy Now, Pay Later (BNPL) services, particularly among young consumers. BNPL offers flexible payment options and convenient access to short-term credit, but it also raises concerns regarding impulsive purchasing, excessive spending, financial literacy, and responsible financial decision making. This research aims to analyze the influence of BNPL services on youth spending behaviour and financial decision making through Artificial Intelligence (AI) and Machine Learning (ML) techniques. The study focuses on young consumers aged 18 30 years and examines key factors such as BNPL usage frequency, purchasing patterns, financial awareness, affordability, and perceived financial risk. Primary data is collected through a structured questionnaire, while secondary data is obtained from existing research literature and financial reports. The proposed methodology involves data preprocessing, exploratory data analysis, and the application of machine learning algorithms to identify behavioural patterns and predict spending-related risks. The study seeks to investigate the relationship between BNPL usage and financial behaviour while exploring the potential of AI driven predictive analytics to support responsible financial decisions. The research is expected to contribute to a better understanding of BNPL-related financial behaviour among youth and demonstrate the potential of machine learning in promoting financial awareness and informed decision-making.

Buy Now, Pay Later (BNPL), Artificial Intelligence (AI), Machine Learning (ML), Youth Spending Behaviour, Financial Decision-Making, Financial Literacy, Predictive Analytics

This study investigated the influence of Buy Now, Pay Later (BNPL) services on youth spending behaviour and financial 7 decision-making using survey-based analysis and machine learning techniques. Primary data were collected from 110 respondents, of whom 73 reported previous BNPL usage. The survey findings indicated that a substantial proportion of respondents perceived BNPL services as contributing to impulsive purchases, increased spending, and changes in purchasing behaviour. Three machine learning algorithms, namely Logistic Regression, Decision Tree, and Random Forest, were implemented to predict self-reported increases in spending. Among these algorithms, Random Forest achieved the highest mean cross-validation accuracy of 72.76% and F1-score of 84.12%. However, the majority-class baseline achieved higher performance, highlighting the limitations of predictive modelling on a small and imbalanced dataset. Feature importance analysis identified BNPL usage frequency as the highest-ranked variable in the Random Forest model. Overall, the study demonstrates the application of AI and machine learning techniques to analysing BNPL-related consumer behaviour while emphasizing the need for larger datasets and more reliable predictive models.

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Sarita Vishwakarma, Anjali Shejal, Vaishnavi Kewadkar, Anjali Mane, Mrs. Snehal Patil (2026). Buy Now, Pay Later: An AI and Machine Learning-Based Analysis of Its Influence on Youth Spending Behaviour and Financial Decision-Making. International Journal of Computer Techniques, 13(6), 685–688. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.24130514