Insurance Sanctions Compliance

Insurance sanctions screening typically begins with matching policyholders, counterparties, and — for marine lines — vessels against government designation lists, primarily the OFAC SDN List, EU consolidated sanctions list, and UK sanctions list. However, effective screening requires going beyond these lists to identify entities that are blocked by operation of law (such as entities 50% or more owned by sanctioned persons under the OFAC 50 Percent Rule), entities with significant network connections to sanctioned actors, and — where relevant — vessels that indicate sanctions evasion risk. Kharon provides the network-level intelligence layer that transforms list-based screening into risk-based screening — surfacing ownership structures, associations, and commercial patterns that designation lists alone do not capture.

The OFAC 50 Percent Rule provides that entities owned 50% or more in the aggregate, directly or indirectly, by one or more blocked persons are themselves blocked — even if they do not appear on any designation list. For insurers, this means that a policyholder, counterparty, vessel owner, or beneficial owner can be sanctioned by operation of law without any government listing. Screening against the SDN List alone will not detect these entities. Kharon’s Sanctions 50% dataset is the world’s most comprehensive and accurate resource for identifying entities blocked under this rule, used by the world’s leading financial institutions and trusted by government enforcement bodies.

The global shadow fleet refers to a growing network of vessels — now numbering in the thousands — that transport sanctioned crude oil and petroleum products outside the visibility of mainstream Western insurance, classification societies, and P&I clubs. These vessels are typically older, poorly maintained, and operate through opaque ownership structures designed to obscure connections to sanctioned actors and programs. For insurers, shadow fleet risk extends beyond the vessels themselves: shared ownership networks, common operators, overlapping shell company structures, and rapid post-designation reorganization can create sanctions exposure even for insurers with no direct shadow fleet coverage. Kharon maps these networks continuously, identifying the fleet-level associations and operator relationships that create indirect exposure.

Sanctions evasion detection requires intelligence that goes beyond name-matching. Effective detection combines beneficial ownership mapping (identifying entities blocked by operation of law, even when not listed), network analysis (connecting entities, vessels, operators, and shell companies across jurisdictions and sanctions programs), and — for marine lines — vessel behavioral analysis (AIS gaps, dark periods, ship-to-ship transfers, re-flagging patterns). Kharon integrates all three — verified by subject matter experts — into a platform that surfaces evasion indicators standard screening tools cannot detect.

An effective insurance sanctions compliance program includes policies and procedures tailored to the organization’s risk profile, sanctions screening at underwriting and renewal, ongoing monitoring during the policy term, claims-stage due diligence procedures, clear escalation and reporting protocols, and regular training. For marine and specialty insurers, the program should also address vessel-level risk assessment, beneficial ownership verification, behavioral monitoring, and multi-jurisdictional compliance (U.S., UK, and EU frameworks each define ownership and control differently). Kharon’s platform supports these workflows end-to-end — from screening and underwriting intelligence through continuous monitoring to claims-stage investigation.

OFAC has issued guidance relevant to insurers across lines of business, including advisories on the 50 Percent Rule, sanctions risks in the maritime petroleum shipping industry, and deceptive practices used to evade sanctions. These advisories identify specific risk indicators — including use of shell companies to obscure beneficial ownership, rapid changes in corporate structure, and for marine lines, AIS manipulation and flag-state changes. OFAC has also emphasized that the 50 Percent Rule applies broadly to entities owned by sanctioned persons, meaning that a counterparty can be blocked even if it does not appear on any government designation list. Kharon’s data is built to operationalize this guidance — surfacing the ownership structures, commercial indicators, and network connections that OFAC’s framework expects insurers to detect.

Beneficial ownership screening is critical for insurers because the OFAC 50 Percent Rule treats entities as blocked when they are owned 50% or more in the aggregate by one or more sanctioned persons — even if the entity itself is not on any government designation list. UK and EU frameworks go further: the UK’s “significant influence” test can apply at ownership levels well below 50%, and EU “control” definitions encompass board representation, management authority, and other non-ownership indicators. For insurers underwriting global programs, the strictest applicable standard governs. Kharon maps high risk beneficial ownership across complex, multi-layered corporate structures — including the opaque jurisdictions, shared directorships, and rapid ownership transfers that characterize both sanctions evasion networks and legitimate but high-risk counterparties — giving compliance teams the visibility they need to satisfy multi-jurisdictional requirements.

Kharon’s research has documented extensive overlap between Russian and Iranian sanctions evasion networks. The same operators, shell companies, and corporate structures frequently service both programs — using shared phone numbers, overlapping addresses, and common directors across jurisdictions like Hong Kong, Oman, and the UAE. For insurers, this means that screening for Russia exposure alone may miss significant Iran sanctions risk, and vice versa. Kharon connects the dots across sanctions programs, mapping these cross-program network relationships that other screening tools cannot see.

P&I clubs face distinct sanctions screening challenges because of their mutual structure and the breadth of their vessel coverage. A single P&I club may provide coverage across thousands of vessels, each with its own ownership chain, operator relationships, and trading patterns. The club must screen not only its direct members but the beneficial owners, operators, charterers, and counterparties associated with entered vessels — across multiple jurisdictions with different ownership and control definitions. Kharon’s continuous monitoring and network intelligence are built for this scale — providing portfolio-level visibility and alerting that keeps pace with the evolving risk landscape rather than relying on point-in-time screening at entry.

Most sanctions screening tools available to insurers aggregate government watchlists, scan media for negative news, and flag name matches. They are designed to answer a binary question: is this entity on a list? Kharon answers a fundamentally different question: is this entity, vessel, or counterparty connected to sanctioned risk through ownership networks, shared identifiers, commercial patterns, or network-level associations? Where standard tools generate high volumes of false positives from suboptimal name matching, every relationship in Kharon’s database has been identified as relevant to a specific risk topic. Where standard tools rely on public ownership records that may be outdated or incomplete, Kharon’s expert-guided research sources and verifies ownership linkages through original investigation. And all data in the Kharon platform is structured to be readable by AI and LLM-powered compliance systems, grounding automated analysis in a reliable, verified source of truth.

Leading insurance compliance programs are increasingly looking to incorporate AI and advanced data analytics to augment human judgment in sanctions screening and risk assessment. Applications include network analysis to map complex ownership and operator relationships, analytics to detect anomalies, and automated monitoring to flag changes in entity risk profiles across large portfolios. The effectiveness of these tools depends on the quality and structure of the underlying data: AI models that rely on incomplete or unverified data produce unreliable results. Kharon’s data is purpose-built for this environment — structured, verified, and designed to be readable by AI and LLM-powered systems to ground results in a reliable source of truth.