The Client
Government agency providing business support and subsidies, facing increasing sophistication in fraudulent activities whilst under pressure to reduce fraud and waste in public programmes.
The Challenge
Rising fraud sophistication required enhanced detection capabilities
Rising sophistication in fraudulent activities required enhanced anti-fraud capabilities with better resource allocation to prioritise high-risk cases.
Existing measures needed complementing with data intelligence approach to identify patterns and anomalies indicating fraudulent activity.
What We Did
Predictive risk scoring with NLP analysis
- Developed predictive risk scoring using logistic regression and interactive binning
- Implemented multi-step process involving data cleaning, feature selection and model training
- Created risk scoring framework for predictive transaction monitoring
- Integrated external data sources and NLP for unstructured data analysis
- Established continuous monitoring and model updates for changing fraud patterns
The Impact
Focused resources on highest-threat cases
£15M
Fraud prevented in year one
60%
Fewer false positives
30%
Earlier fraud detection
Prevented £15M in fraudulent claims whilst reducing false positives by 60% and improving early detection by 30%. Enabled focus on highest-threat cases, delivering significant efficiency gains in public fund protection.
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