Consumer borrowing behaviour is changing with the evolution of digital finance, specifically, the customization of banking and Buy Now, Pay Later engines (BNPL) that work mainly with the help of Artificial Intelligence (AI). They make the process of payment very convenient and flexible, which also provides a chance to enter addictive borrowing, which especially concerns younger generations, including the Millennials and the Generation Z. This paper is conceptualized to equate AI-based digital finance in the provision of covert debt traps that pair the primal behavioural antecedents such as Fear of Missing Out (FOMO) and technological improvements and new forms of micro-credit. Based on the behavioural finance, psychological models of decision-making, and digital well-being views, the framework can provide the interplay between AI-based urgency indicators and FOMO and compulsive buying decisions that are more mediated by BNPL services. The article suggests the research directions in terms of the interaction between the psychological vulnerability, algorithmic persuasion and innovative credit structure, as it points out the differences between the generations. The implications underline the theoretical worth of the study to the consumer behaviour, managerial role in the provision of ethical fintech, and the necessity of regulating to prevent the misuse of the algorithms to enumerate the biases of the consumers. Finally, this kind of idealization shows the dire need to focus on the socio-financial impact of the digitalization of personalization in credit systems.