Predictive Analytics in Anti-Corruption and Public Financial Oversight: Advances and Opportunities

Abstract

Corruption drains public resources, distorts the allocation of contracts and services, and erodes the legitimacy of governments. The digitisation of public administration (electronic procurement, integrated financial management information systems, open contracting portals, and beneficial ownership registers) has produced large, structured administrative datasets that make corruption and financial mismanagement increasingly measurable and, in principle, predictable. This paper surveys the advances and future opportunities of predictive analytics for anti-corruption and public financial oversight. We synthesise the state of the art across three dimensions: the data sources that now support risk analysis, including procurement records, budget and spending data, and ownership information; the analytical methods used to identify and rank risk, including red-flag indicators, statistical anomaly detection, supervised machine learning, and network science; and the operational use cases in audit, oversight, and early-warning systems. We present a described taxonomy that maps common corruption risk indicators to the analytical techniques and data requirements that operationalise them. We then examine the open challenges that constrain real-world deployment: fragmented and low-quality data, the tension between predictive flagging and due-process guarantees, the demand for explainability in high-stakes public decisions, and the adaptive gaming of detection rules by strategic actors. Finally, we outline a research and practice agenda spanning interoperable open data standards, interpretable and auditable models, causal and counterfactual evaluation, human–machine oversight workflows, and governance safeguards that protect rights while improving detection. We argue that predictive analytics is best framed not as an automated verdict machine but as a triage and prioritisation instrument that directs scarce investigative and audit capacity toward the highest-risk cases.