1) Modern tax and welfare administrations are leveraging new data sources and advanced analytics to improve targeting of fraud risks and non-compliance. This involves taking a segmented approach using techniques like anomaly detection, predictive modelling, and social network analysis.
2) Fraud ranges from opportunistic to organized crime. Responses require differentiation based on fraud type and impact. Hybrid analytical approaches overlay multiple techniques to comprehensively identify potentially fraudulent transactions, entities, and networks.
3) Governments must become more data-driven and mature in their use of analytics. This involves moving from rule-based checking to risk-based prioritization and treatment using predictive risk scoring. Advanced analytics are being applied to business processes to prevent fraud and