This chapter aims to provide a comprehensive analysis of fraud risks associated with the rapidly expanding domains of digital lending and Buy Now, Pay Later (BNPL) models. While these fintech innovations offer unprecedented convenience and financial access, they also expose significant vulnerabilities due to their operational frameworks, speed of disbursal, and limited credit vetting. BNPL platforms, in particular, are increasingly attractive to fraudsters because of their reliance on minimal credit checks and deferred repayment windows. As a result, both digital lending and BNPL ecosystems have become fertile ground for a range of fraud typologies including identity theft, synthetic identity fraud, account takeovers, and first-party frauds. The chapter will analyze these common fraud types, drawing attention to their unique manifestations in digital credit environments. It will then explore advanced fraud detection and prevention strategies, with a strong focus on Artificial Intelligence (AI) and Machine Learning (ML). These technologies enable platforms to analyze vast volumes of real-time transaction data and identify subtle fraud patterns that often go undetected by traditional, rule-based systems. The key techniques to be examined include AI-powered transaction monitoring, Risk-based decision-making and Behavioral analytics that track nuanced user interactions to distinguish between genuine and suspicious behavior. In addition to technological solutions, the chapter will discuss the evolving regulatory and compliance landscape, emphasizing the growing responsibility of financial institutions to implement robust fraud prevention systems. By combining technical insights with practical implications, this chapter will offer valuable guidance for academics, fintech professionals, and policymakers seeking to strengthen fraud resilience in the digital lending and BNPL sectors.