AI-driven recommendation engines increasingly shape consumer financial behavior by personalizing product offerings across banking, insurance, credit, and investment platforms. While these systems are designed to improve decision efficiency and reduce information overload, their widespread integration raises concerns regarding financial choice diversity, market competition, and consumer autonomy. At a broad level, recommendation engines function by learning from historical transaction data, behavioral patterns, and demographic attributes to predict and suggest financial products that align with user profiles. However, because these engines prioritize optimization for engagement, conversion, and risk minimization, they may implicitly reinforce existing financial behaviors rather than broaden consumer choice sets. Over time, this can lead to algorithmic narrowing, where users are continuously steered toward similar product categories such as standard credit lines instead of alternative community lending schemes or low-volatility funds instead of emerging asset opportunities. From a market perspective, platforms with dominant data access and model accuracy may disproportionately influence consumer financial pathways, contributing to market concentration and reduced visibility of smaller or innovative financial providers. Additionally, feedback loops in recommendation systems can create self-confirming financial risk profiles, where consumers perceived as higher risk are persistently directed toward costlier or lower-reward products, exacerbating inequality. At the individual psychological level, consumers may experience a gradual erosion of active financial comparison and autonomous evaluation skills, relying instead on system-generated “best match” options. This paper evaluates whether recommendation engines inherently limit or can be restructured to enhance choice diversity, proposing transparency protocols, explainability layers, and diversity-aware optimization metrics to preserve consumer financial autonomy while sustaining decision efficiency.