AI-powered Fraud Detection Solution to Improve Supply Chain Transaction Performance
Strengthening the audit analytics function of a leading Pharma client through proactive risk identification, bringing increased transparency and improved operational efficiency.
About the Customer
The customer is a leading Pharma company based out of New York, with valuation of over $USD25 Billion and headcount over 20,000.
The organization had been investing significant time addressing business disruptions caused by fraudulent vendor transactions.Fraudulent transactions impact the supply chain in the following ways:
Overall increased costs
Brillio implemented an AI powered – Real Time Vendor Transaction Anomaly Detection Solution through which potential fraudulent vendor transactions can be identified and profiled in near real-time, thereby enabling rapid intervention. This solution also provided quantification of risks associated with adding new vendors,which tend to bring more risk.
The 4 pillars that form the crux of this AI Solution are as following: –
Build Clustering models to identify potential anomalous transaction clusters
Apply statistical techniques on these identified clusters to derive key indicators of fraud
These key fraud indicators are then leveraged to produce the final fraud risk score
Continuous vendor tracking to provide early warning signals about new vendor activities.
Assignment of a risk score to new vendors based on statistical similarity with existing ones
4.Automated Model Refresh and Maintenance
Periodical refresh of the Machine Learning (ML) models with human-feedback loops
Business Benefits and Impact:
The AI-powered fraud detection solution enabled:
Real-time monitoring and interpretation of rapidly changing vendor transactions,and the ability to immediately respond to potential breaches that might have an adverse effect on the system
Intelligent vendor onboarding that helps improve vendor quality through a ML-generated Fraud Risk Score
Increased operational efficiencies by reducing the effort needed to identify and analyze potential fraudulent transactions by 50x