Not a case this time, a detection method. “Ghost vendors” and phantom employees survive on paper: registration documents and invoices are easy to manipulate, and traditional review checks the paper, not the physical world behind it.
Cross-referencing vendor registration and billing data against open source geolocation mapping closes that gap. A supplier whose corporate headquarters maps to a residential cul-de-sac, a vacant lot, or a short-term rental is a flag worth raising before the first payment clears, not in a post-mortem.
Routine business health checks used to mean manually digging through spreadsheets and ledgers for weeks to find anomalies. Today, automated risk assessment is completely changing that dynamic.
One of the most powerful and underutilized tools for proactive detection is open source geolocation data. When investigating ghost vendors or phantom employees, traditional paper trails and invoices are easily manipulated. However, cross-referencing registration and billing data with open source geolocation mapping instantly highlights physical anomalies. If a new supplier's corporate headquarters maps directly to a residential cul-de-sac, a vacant lot, or a short-term rental property, an automated system can flag the risk before a single fraudulent payment is ever processed.
We are currently building a risk assessment engine that integrates these exact types of open source data points to automate the investigative heavy lifting. The goal is to provide organizations with a continuous, automated health check rather than a reactive post-mortem.
I've spent 15+ years investigating financial crime in law enforcement, fintech, and the credit bureaus. I write these briefs so the patterns are harder to miss. If this one hits close to home, I'm easy to reach.