AI-Native Asset Management: A Product Strategy Framework for Intelligent Investment Platforms
DOI:
https://doi.org/10.22399/ijcesen.5520Keywords:
AI-Native Fiduciary Platforms, ANFP framework, platform governance, intelligent investment architecture, public-domain platform assessmentAbstract
Artificial intelligence (AI) native asset management is a new generation of investment platforms that integrate prediction, portfolio construction, execution, personalisation and governance into a single product architecture. This review brings together existing peer-reviewed research in financial economics, empirical asset pricing, machine learning, Generative AI/Gen AI, and reinforcement-learning trading, financial textual analysis, delegated portfolio management, robo-advisory systems and platform strategy to analyze the foundations and evolution of AI-based investment decision-making. The review highlights the roles of supervised learning, deep learning, reinforcement learning, volatility forecasting, robust optimization and platform-governance theories in asset-management applications. Although existing techniques have improved return prediction, alternative-data analytics, and financial-text interpretation, the use of alternative data in prediction, and in the understanding of financial text, practical deployment remains constrained by estimation error, false discovery risk, transaction costs, market non-stationarity, model opacity, and fiduciary obligations. This study proposes the AI-Native Fiduciary Platforms (ANFP) framework, a strategic model developed by reviewing the literature, which categorises the intelligent investment platforms based on six important design principles: economically constrained prediction, modular intelligence, governed learning, decision-relevant personalisation, explainability and traceable explanation, and platform-level performance measurement. The current analysis shows that the research is still very focused on the prediction and trading aspects and very little on integrated management of client outcomes, execution feedback loops and governance mechanisms. The review highlights a number of research avenues with potential for further progress: counterfactual evaluation of platforms, interpretable fiduciary intelligence, fairness-aware suitability assessment, robust optimization under regime uncertainty, execution-aware learning architectures, and safe protocols for experimentation. The study finds that the widespread adoption of AI in asset management is not about a single predictive model, but a controlled fiduciary decision-making process of predicting, optimizing, executing, personalizing and overseeing, within an intelligent fiduciary system.
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