Forecasting Household Economic Hardship, Financial Fragility, and Vulnerability Through Artificial Intelligence and Predictive Analytics

Abstract

Household economic hardship in the United States is increasingly shaped by interacting pressures rather than a single income shortfall. Inflation, rent burdens, revolving debt, medical costs, labor-market volatility, and fraud losses combine to produce financial fragility that conventional scorecards often miss. This paper develops a publication-ready research framework for forecasting household economic hardship, financial fragility, and vulnerability through artificial intelligence and predictive analytics using public data through 2024. The framework integrates survey microdata and contextual indicators from the Federal Reserve’s Survey of Household Economics and Decisionmaking, the Consumer Financial Protection Bureau’s Making Ends Meet surveys and Financial Well-Being Scale, the U.S. Census Bureau’s Household Pulse Survey, USDA food security reports, the Federal Reserve’s Survey of Consumer Finances, Bureau of Labor Statistics expenditure and inflation statistics, and Federal Trade Commission fraud-loss data. Building on the supplied literature and broader scholarship in household finance, explainable machine learning, and welfare measurement, the paper proposes a multimodal architecture that combines gradient boosting, temporal models, and graph-informed neighborhood context to predict three linked outcomes: immediate hardship, near-term fragility, and medium-horizon vulnerability. Data trends through 2024 show that the share of adults doing at least okay financially declined after 2021, emergency liquidity weakened, food insecurity rose, and reported fraud losses climbed sharply. The paper argues that explainable, fairness-audited models can improve targeting, consumer protection, and preventive intervention without reducing households to opaque risk labels. It concludes that AI can strengthen public-interest forecasting when embedded in transparent validation, governance, and human-centered implementation.