The Household Income and Expenditure Survey is one of the most important statistical instruments any nation operates. It powers poverty measurement, the Consumer Price Index, cost-of-living analysis, social policy targeting, and the economic indicators ministries rely on to set direction. Conventionally, HIES is produced through long, expensive, periodic surveys — instruments that are accurate but slow, manual, and increasingly out of step with the speed of policy decisions in modern economies.

NCSI's HIES, like its peers across the GCC and beyond, faced a structural tension: the data is precious and the methodology is sound, but the delivery cadence cannot keep up with the speed of policy. To meet this brief, NCSI needed a partner that could combine deep AI/ML expertise with rigorous data-governance discipline and direct, on-the-ground implementation in Oman.

AiSPRY, in partnership with ODP Oman, was scoped to deliver the world's first national-level transition of an entire HIES from traditional survey-based analysis to AI/ML-driven estimation — while preserving statistical rigour, explainability, and the legal data-protection framework required for sovereign national statistics.

AiSPRY designed and is delivering the HIES AI Platform — a five-layer, cloud-native AI/ML system built around four foundational pillars and a CRISP-ML(Q)-aligned methodology. The platform ingests historical HIES microdata, macroeconomic indicators, utility usage, and administrative feeds; cleans and harmonises them to international classifications (COICOP); engineers household-level features; and trains an ensemble of forecasting and regression models.

Every output runs through an explainability and bias-audit layer before being surfaced through a bilingual dashboard, REST API, and scenario-simulation tools for NCSI and partner ministries. The four pillars: AI-Driven Estimation (continuous ML-driven income and expenditure estimation, targeting 70–80% reduction in time to produce statistics); Predictive Analytics (forecasting across multiple horizons for evidence-based planning); Proactive Insights (real-time bilingual cross-ministry intelligence); and Explainable AI (100% of predictions explained via SHAP, with audit trails, bias and fairness checks, and back-testing against the 2010 HIES).

The modelling stack combines ARIMA, Prophet, and LSTM for macro-level time-series, XGBoost and LightGBM for household-level expenditure and income, Random Forest and logistic regression for segmentation, and K-means for household clustering. A synthetic-data generator expands the prototyping volume to 50,000 statistically faithful households, preserving the privacy of the underlying ~5,660 real records.