Business Intelligence Dashboards for Household Financial Planning Tax Management and Decision Making

Abstract

Purpose: Personal finance applications now aggregate bank, card, loan and tax data into dashboards that promise better household decisions, yet their evaluation rarely goes beyond engagement and satisfaction. This review asks which dashboard design properties improve the quality of household cash-flow, debt, saving and tax decisions, and under which conditions they produce over-reliance or privacy harm. Method: An integrative review of household finance, behavioral economics, visual analytics, fiscal psychology and data governance research published to 2025 was conducted under a documented search, screening and appraisal protocol, with contradictory findings adjudicated by design strength and outcome proximity. Findings: Evidence converges on four conditions: reconciled data, perceptually efficient encodings matched to the user's visualization literacy, explicit uncertainty on forecasts and tax estimates, and prompts placed at the moment of choice. Evidence diverges on whether richer displays or simple rules of thumb serve low-literacy households better. Contribution: The article proposes the LEDGER framework (linked data, encoding fit, decision anchoring, guided action, exposed uncertainty, rights over data), defines decision-quality metrics including dominance errors and Brier-scored shortfall judgments, states six falsifiable propositions, and specifies a two-stage validation design. Implications: Providers and regulators should judge household dashboards by decision quality and harm incidence rather than logins or feature counts.