The study evaluates 13 economic indicators across six countries (India, Philippines, Thailand, France, UK, US) from 2000–2023, detecting anomalies, structural breaks, and outlier behaviour. It employs Benford’s Law, Grubbs’ Test, Chow Test, and DBSCAN on data from the Global Macro Database, using a contingency-based approach to validate anomalies through methodological convergence. Anomalies often correspond with periods of political transition or institutional volatility, emphasising the impact of political risk, institutional fragility, and data governance, especially in developing economies. The study offers a reproducible and scalable methodology for auditing official economic statistics, supported by a review of literature on economic measurement, data analytics, and political risk.

Economic loss caused by fraud has become a subject of concern for countries globally. Digital world also provides data and these can be leveraged to detect and prevent fraud while also applying forensic analytics to recover the loss. Although gathering and collating data from various sources poses a challenge, the benefits outweigh the costs. Data analytics, if implemented correctly, may detect fraud and prevent a potential economic loss. The article discusses challenges, solutions and technologies for implementing a data-driven approach.