From Detection to Prevention: Risk-Based Predictive Analytics in Fraud Management
Industry: Government Agency
Challenge: Advancing Anti-fraud Capabilities
Solution: Predictive analytics, fraud detection, data management
Technology: SAS Enterprise Miner, Oracle, Python
Situation and Challenge
A government agency providing business support and subsidies sought to enhance its anti-fraud capabilities amidst increasing sophistication of fraudulent activities. The agency faced several challenges:
- Pressure to reduce fraud and waste in government programmes
- Optimising resource allocation to prioritise high-risk cases for investigation
- Adapting to evolving fraud patterns with a complementary solution to existing anti-fraud measures
Insight and Action
Our approach included:
- Developing predictive risk scores using logistic regression and interactive binning techniques for robust predictive risk scoring
- Implementing a multi-step feature engineering process involving:
– Data cleaning, transformation and normalisation
– Feature selection and importance determination
– Adaptive risk thresholding through binning
– Model training, validation and refinement
- Integrating external data sources for contextual enrichment
- Natural language processing for unstructured data analysis
Our intervention delivered improvements, including:
– Enabled prioritisation of high-risk cases for investigation, with 60% reduction in false positives
– €15 million in prevented fraudulent claims within the first year
– 30% improvement in early fraud detection, enabling proactive intervention
– Improved efficiency gains, contributing to future cost and resource savings
Call us On +44 (0) 208 004 3015
Related Content.
