Top Fraud Clusters
Where the network shows the highest concentrated risk
These are real address-based clusters surfaced automatically from the SentinelGraph network — multiple subject organizations sharing infrastructure across millions of dollars in tracked exposure. Names and street numbers are masked for public preview; subscribers see the full underlying records.
| # | Address (masked) | Subjects | Tracked $ | Pattern | Sector |
|---|---|---|---|---|---|
| 1 | ███ Wilshire Blvd, Los Angeles CA | 12 | $90.05M | Healthcare clinic chain — 4 distinct addresses, 9-12 dedup'd subjects each | Healthcare |
| 2 | ███ S Wabash Ave Ste ████, Chicago IL | 9 | $66.62M | Multi-state nonprofit network — 7 connected branch addresses | Housing |
| 3 | ███ Willis Ave, Mineola NY | 5 | $40.00M | Family-services nonprofit — 2 connected NY addresses | Housing |
| 4 | ████ E 11 Mile Rd, Madison Heights MI | 35 | $39.83M | Religious-affiliated multi-location nonprofit | Housing / Cross-Sector |
| 5 | ███ Chapel St, New Haven CT | 25 | $33.51M | Religious-affiliated multi-location nonprofit | Housing / Cross-Sector |
| 6 | ███ Broadway, New York NY | 28 | $32.51M | Religious-affiliated multi-location nonprofit | Housing / Cross-Sector |
Why address-cluster signals matter
When the same address appears in 5, 10, 25+ records across different subject organizations, that's rarely coincidence. It's a structural signal: a registered-agent service, a holding-company hub, a branch network, or — most concerning — a single operator working through layered corporate structures.
SentinelGraph surfaces these clusters automatically by joining 21.6 million observed co-location records against our scored subject set. Subscribers see the full underlying records, the connected subjects' risk scores, and the path through the graph that produced the signal.
Note on signal quality: top results are weighted by number of distinct subjects co-located. Some clusters above include legitimate multi-branch nonprofit networks (Catholic Charities, Pacific Clinics, etc.) — name canonicalization across branches inflates those counts. Subscribers can filter by data-source provenance and entity-resolution confidence to focus on truly suspicious patterns vs. branch-network artifacts.
Important disclaimer: SentinelGraph provides structured intelligence based on publicly available data. It does not provide legal advice, conduct investigations, or make determinations of fraud, liability, or regulatory violation. All findings are preliminary, evidence-traceable observations intended to support — not replace — qualified legal, investigative, or compliance review. Use of this service does not create an attorney-client relationship or any professional engagement unless separately agreed in writing.
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If you work in healthcare compliance, fraud investigation, or legal oversight — and you want to understand how exclusion-linked control risk may affect entities in your portfolio — SentinelGraph may be able to help.
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