Crypto investment platforms rely on predictive algorithms that analyze past blockchain activity, market patterns and conditions, and user behavior to surface specific assets and encourage user engagement. These systems combine on-chain signals, market microstructure features, and user profiles to rank tokens and personalize messages. Based on the established user profile, predictive algorithms push personalized notification content during a user’s habitual activity window. As a result, predictive models increasingly determine how retail investors encounter information. As these systems increasingly rely on AI-driven ranking, personalization, and automated prompts, the investor decision-making environment has shifted away from traditional, human-mediated financial guidance. This poses potential risks for investors due to a lack of transparency related to the algorithm, conflicting with disclosure and potentially accountability.  Disclosure and suitability regimes that were built for and evolved within environments for human-mediated brokerage strain when personalization and ranking shift material choices into a system layer. This paper examines the impact of predictive algorithms on the crypto investment landscape and the subsequent need for evolving disclosure, suitability, and accountability policies and regulatory frameworks. The paper develops a qualitative framework supported by market context, relevant court rulings, and cross-jurisdictional regulatory guidance on privacy, accountability, intellectual property, consumer protection, and governance. It examines how these principles apply to human oversight and system-level accountability within predictive models, to mitigate algorithmic investor risk and strengthen transparency.