At first, the participant record didn’t add up.
We found an obituary that matched perfectly on name and age, but the locations didn’t align.
The participant’s pension record tied him to Louisiana, where he held a teacher’s account.
The client’s data had his address in Illinois.
And the obituary? It was published in New York.
For most automated processes, that’s where the trail ends—a fragmented record, easily overlooked, and one that could have allowed payments to continue unchecked.
For our team, it was where the work began.
Rather than dismissing the record or forcing a match, we expanded the search. That’s when we uncovered an unexpected clue: a biographical article published in the Rwanda New Times.
Yes—Rwanda.
The article outlined the individual’s academic career and, in doing so, connected every loose thread. He earned his PhD in Illinois, later taught at a university in Louisiana, and maintained professional and personal ties that explained the New York obituary.
What looked like conflicting data points were, in fact, pieces of the same story.
Illinois. Louisiana. New York. Rwanda.
Each location was real. Each was relevant.
This match required more than a dataset or an algorithm—it required context, breadth of sources, and human validation willing to follow the evidence wherever it led. It required a company and a team of human experts who are dedicated to validating every single death correctly.
These are the cases that don’t announce themselves. They go quietly unresolved, until someone takes the time to connect the dots.
That’s the work behind accurate outcomes.
And that’s why every connection matters.
