AI-Enabled FinTech and Investment Decision-Making: An Integrative Review of Capabilities, Behavioural Adoption, and Governance

Artificial intelligence (AI) has moved from a back-office efficiency tool to a component of the decision architecture through which investments are researched, recommended, and executed. This review synthesises contemporary academic and authoritative institutional evidence on how AI-enabled financial technology (FinTech) reshapes investment decision-making, and on the behavioural, methodological, and regulatory conditions that determine whether that reshaping improves outcomes. The study adopts an integrative, thematic review approach, drawing on peer-reviewed studies, working papers, and primary regulatory and industry documents published predominantly between 2018 and 2026. Evidence is organised around five application streams—robo-advisory, machine and deep learning for prediction and portfolio construction, natural-language processing (NLP) and sentiment analytics, generative AI and large language models (LLMs), and the behavioural adoption of algorithmic advice—and is then read against a cross-cutting governance and risk layer. AI systems demonstrably widen the information set, automate signal generation, and mitigate several documented investor biases, and field evidence links robo-advice to higher equity participation, improved diversification, and better risk-adjusted returns, particularly for smaller and less sophisticated investors. Yet gains are uneven and conditional: statistical predictive superiority does not always translate into portfolio gains, algorithm aversion suppresses adoption for large investor segments, and opacity, herding, data-quality, and accountability risks are recognised across regulators. Explainability and hybrid human–AI designs recur as the pivotal moderators of trust. For practitioners and policymakers, value from AI-enabled FinTech depends less on model sophistication alone than on transparency, human oversight, data governance, and calibrated trust. The review consolidates an integrative framework and a research agenda for the human–AI hybrid investment decision system.