Case Study: How Kroll’s Investigators Use AI-Powered Ownership Mapping to Build Cases Faster
Kroll’s clients don’t need a compliance check. They need a corporate X-ray.
The Challenge
Kroll’s work is adversarial. The targets of its investigations — acquisition sellers, litigation counterparties, suspected fraud operators — are often actively trying to hide ownership. Unlike a bank performing routine KYB, Kroll is trying to uncover what someone has deliberately concealed.
This creates a different kind of problem. A compliance tool that maps transparent ownership structures is useful but limited. What Kroll needs is a tool that detects the structural signatures of concealment: circular ownership loops where Company A owns Company B which owns Company A, nominee directors provided by the same corporate service provider across multiple unrelated entities, ownership fragmented at exactly 24.9% per shareholder to stay below the 25% UBO threshold.
The data collection phase of a typical Kroll investigation consumed the majority of analyst time. Investigators would manually search company registries across jurisdictions, build ownership charts in spreadsheets, calculate indirect ownership percentages by hand, and often miss structural anomalies because they were tracing chains in a single direction. The investigative judgment — the analysis that Kroll’s clients actually pay for — came last, after days of data collection.
The Solution
Kroll deployed Zavia.ai as an investigative acceleration layer that handles the data collection and structural analysis, so investigators start their work with a complete corporate map already in hand.
How Zavia.ai works differently for Kroll:
- Concealment pattern detection: Unlike compliance-focused UBO tools that assume ownership structures are transparent, Zavia.ai’s algorithms are trained to identify the structural signatures of deliberate concealment. Circular ownership, nominee patterns, threshold avoidance, and entities registered at known corporate service provider addresses are flagged automatically — directing investigators immediately to the parts of the structure designed to hide the real controller.
- Multi-target investigation support: Kroll’s investigations often involve not one target but a network of related entities. A fraud investigation might involve mapping 50 or 100 companies to identify common ownership, shared directors, or financial links. Zavia.ai can resolve ownership chains for multiple entities simultaneously and surface connections between them — shared UBOs, common directors, overlapping addresses — that would take investigators weeks to find manually.
- Client-ready ownership visualizations: Kroll’s deliverable to clients is often a corporate structure map at the center of a report. Zavia.ai produces interactive ownership visualizations that show every entity, every ownership path, and every UBO in a format that investigators can annotate, export, and include directly in client deliverables. This eliminates the hours typically spent building corporate charts manually.
- Litigation-grade evidence: In disputes and enforcement matters, Kroll’s findings must withstand legal scrutiny. Every data point in Zavia.ai’s ownership maps links back to the originating government registry, with timestamps and source identifiers. This provides an evidence trail that is defensible in court proceedings, arbitration, and regulatory hearings — not a commercial database output that opposing counsel can challenge as unverified.
The Results
| Metric | Before Zavia.ai | After Zavia.ai |
| Initial ownership mapping | Days of manual registry searches per target entity | AI-generated in minutes, including concealment pattern flags |
| Multi-entity network analysis | Weeks to map connections across 50+ related entities | Simultaneous resolution with shared-UBO and shared-director detection |
| Circular ownership detection | Frequently missed — analysts trace ownership in one direction | Detected automatically by AI bidirectional analysis |
| Client deliverable production | Hours of manual chart building per investigation | Export-ready interactive ownership visualizations |
| Evidence standard | Analyst notes + database screenshots | Government registry-sourced with timestamps and source identifiers |
Why It Matters
Kroll’s investigators are hired for their judgment, not their ability to search company registries. Every hour an investigator spends on data collection is an hour billed to the client for low-value work. Zavia.ai inverts that ratio. By the time a Kroll analyst opens a case file, the corporate structure is already mapped, the anomalies are already flagged, and the evidence is already linked to official sources.
For Kroll’s clients — law firms authorizing multi-million-dollar acquisitions, private equity funds assessing target companies, multinational corporations in litigation — this means faster investigations with more thorough results. The AI catches structural patterns that even experienced investigators miss when tracing chains manually across jurisdictions.
Bottom line:
Zavia.ai accelerates Kroll’s corporate investigations by automating the data collection phase, detecting concealment patterns that manual research misses, supporting multi-entity network analysis, and producing litigation-grade evidence linked directly to government registries.
