AI-Driven Personalization in Robo-Advisory: A Conceptual Framework for Integrated Household Financial Decision-Making

Abstract

The vast majority of households lack access to integrated professional financial advice, leading to systematic and costly errors in investment, borrowing, and retirement planning. Contemporary robo-advisors address accessibility by automating portfolio allocation but remain structurally limited: they optimize investments in isolation from credit risk, debt obligations, and household cash-flow constraints, rely on simplistic and biased risk-tolerance questionnaires, and fail to resolve the cold-start problem for new users. This paper develops a novel conceptual framework for AI-driven, integrated household financial advisory systems grounded in design-science research. The framework comprises four tightly coupled modules—Credit Risk Assessment, Cash-Flow Forecasting, Dynamic Portfolio Optimization via reinforcement learning, and Financial Planning and Recommendation—linked through a Personalization Cascade. In this cascade, the credit risk profile (risk coefficient, debt-to-income ratio, and income volatility) parameterizes downstream modules, producing advice that is compoundingly personalized rather than merely additive. A second core contribution, Credit-Risk-Informed Utility Initialization, operationalizes the cold-start problem by seeding the reinforcement learning agent’s utility function with behavioral signals from credit data, ensuring that households with different financial constraint profiles face genuinely different optimization landscapes from the first interaction. Three theoretical propositions on the interdependence of household financial decisions, the superiority of credit risk as a personalization signal, and reinforcement learning as the integration mechanism underpin the architecture. Illustrative personas and a capability comparison with leading platforms (Betterment, Wealthfront, Scripbox, Groww) demonstrate that the framework generates structurally different—and welfare-superior—recommendations, particularly the explicit debt-repayment versus investment trade-off. The framework offers a theoretically grounded blueprint for next-generation robo-advisory systems that can improve financial inclusion, reduce costly household errors, and meet emerging regulatory expectations for explainable, integrated advice.