ENTITY RISK SCORE · PILLAR 3 OF 4

Related Entity Risk.

15% of total score Exposure through who you trade with

Assesses risk based on connections and correlated activity with related entities, markets, and instruments — the strongest bidirectional connection drives the component.

WHY IT MATTERS

Risk rarely exists in isolation. Entities, accounts, instruments, and venues are interconnected.

Shared instruments or trading patterns across entities

Common counterparties or intermediaries

Coordinated behavior across time, markets, or asset classes

Network centrality that amplifies impact

Related Entity Intelligence helps you see around corners — early.

RELATIONSHIP NETWORK EXAMPLE

Strong (70+) Moderate (40–69) Numbers = confidence score (0–100) · synthetic data

HOW RELATED ENTITY RISK IS CALCULATED

1. IDENTIFY CONNECTIONS

Map direct and indirect relationships between entities, accounts, instruments, markets, and venues.

2. MEASURE OVERLAP & EXPOSURE

Evaluate shared activity, instruments, counterparties, and trading behaviors.

3. SCORE RELATIONSHIP STRENGTH

Apply confidence scoring based on overlap, frequency, recency, and behavioral similarity.

4. CALCULATE CORRELATED RISK

Aggregate connected activity to determine correlated exposure and network impact.

The result: a Related Entity Risk Score that reflects how connections amplify potential harm.

KEY CORRELATION DRIVERS

Shared Instruments — Common securities, derivatives, or product exposure

Common Counterparties — Same brokers, market makers, or clearing relationships

Trading Pattern Similarity — Similar timing, size, price, or behavioral signatures

Cross-Market Activity — Related activity across venues, markets, or asset classes

Information Links — News, events, or data that connect entities

CORRELATION SIGNALS

Named correlation signals whose status is derived by the documented predicate in each evidence expansion.

SIGNALWHAT MNC CHECKS

Known Collusive Group linkagePairs carrying the Known Collusive Group relationship type.

Common-counterparty hubsCounterparties connected to two or more carried primary entities.

Confirmed identity linkageUBO / LEI attribution records carrying Confirmed status.

Synchronized same-day activityPairs with two or more relationship records on one activity date.

RELATIONSHIPS — BY FAMILY

The four canonical relationship families, derived from the governed pair set in the current scope. Illustrative synthetic counts.

FAMILYPAIRSCONNECTED PARTIESMAX STRENGTH

Correlated Activity & Shared Exposure11769%

Trading / Counterparty Relationships4391%

Operational Connections5594%

Account Ownership & Attribution44100%

THE RELATIONSHIP LAYER

Exactly what the relationship layer looks for.

Eight evidence families. A connection becomes more analytically meaningful as independent dimensions reinforce one another.

EVIDENCE BEFORE CONCLUSION

Identity & Control Linkage

Are the entities linked by ownership, control, trader, strategy, desk, or another governed organizational relationship?

Same Beneficial OwnerSame TraderSame Algorithm / Strategy — taggedShared Desk / Business Unit

Evidence: Entity master / reference mapping, ownership or control lineage, trader/strategy assignment, desk or business-unit mapping.

Why it matters: A structural relationship can make otherwise separate alert activity analytically relevant to one another.

Repeated Counterparty / Transaction Linkage

Do the same entities repeatedly appear opposite one another or within a recurring transaction chain?

Frequent Counterparty PairingCircular ChainRepeated opposite-side interaction

Evidence: Entity pair frequency, matched or opposite-side activity, recurring pair interaction, transaction-chain evidence.

Why it matters: Repeated pairing can be more meaningful than one isolated interaction, but it remains a relationship indicator — not proof of coordination.

Shared Security / Instrument Exposure

Are multiple entities repeatedly active in the same security or economically related instruments?

Shared SecuritiesRelated InstrumentsUnderlying ↔ DerivativeCash ↔ FuturesETF / Component linkage

Evidence: Security identifiers, related-instrument mapping, shared positions/activity, cross-product context.

Why it matters: Common exposure helps determine whether the relationship is concentrated in one name or extends across linked products.

Shared Market / Venue Context

Do the entities repeatedly overlap in the same market, venue, or cross-market pathway?

Shared MarketsCross-Market ActivityVenue Participant Linkage

Evidence: Market / venue identifiers, cross-market activity, overlap sessions, venue-participant reference data.

Why it matters: Market overlap adds context, particularly when it repeats alongside other relationship dimensions.

Shared Risk Theme / Alert Logic

Do the entities repeatedly trigger the same Risk Group or the same canonical Alert Model?

Shared Risk GroupsShared Alert ModelsSimilar behavioral pattern

Evidence: Canonical Risk Group, Alert Model, supporting alert records, pattern evidence.

Why it matters: Different entities independently pointing to the same surveillance theme can strengthen the analytical connection.

Temporal Overlap

Does related activity occur on the same dates, in the same windows, or repeatedly near one another in time?

Shared Time WindowsRepeated co-occurrencePeak-date overlapSession overlap

Evidence: Alert dates, start/end windows, market sessions, recurring common time-of-day behavior.

Why it matters: Timing proximity helps distinguish a persistent relationship from two entities that merely share a market or security.

Directional Relationship Shape

Does the activity appear positively related, potentially complementary, or potentially inverse?

Positively related activityComplementary activityInverse activity

Evidence: Direction of activity, related-security context, pair behavior across common dates/windows, supporting alerts.

Why it matters: MNC may characterize the shape of the relationship, but it does not infer intent or coordination from the shape alone.

Prior Reference / Case Linkage

Is there an existing controlled internal reference that materially connects the entities?

Prior Case LinkageKnown internal relationship reference

Evidence: Governed reference linkage and lineage to the underlying record.

Why it matters: Prior linkage can strengthen context, but the present analytical evidence must still stand on its own.

RELATIONSHIP INTELLIGENCE

Coordination that single-entity tools never see.

Every connection is measured bidirectionally — connection strength, shared securities and markets, observation intervals, and directional classification. The strongest bidirectional connection drives the Related-Entity component of the composite score (MNC baseline default: 15%, customer-configurable).

That means exposure can arrive through who you trade with: an entity with modest evidence of its own still surfaces when it is strongly connected to a high-risk counterpart — and positioning in correlated names ahead of another company's event is exactly the pattern single-entity review misses.

RELATED-ENTITY EXPOSURE — ILLUSTRATIVE 15% COMPONENT

Entity A ↔ Entity B Strength 82

Shared: 2 securities · 1 market Bidirectional Observed 30d

Entity A → Entity C Strength 64

Shared: equity ↔ options pair Directional Observed 10d

Entity A ↔ Entity D Strength 41

Shared: 1 market Correlated timing Observed 30d

Connection strength, shared scope, and direction on synthetic data. Network graph engines are future phase.

HOW IT FEEDS THE COMPOSITE

50%

Alert Derived Risk

30%

Behavioral Pattern Risk

Configurable

Related Entity Risk

This pillar · MNC baseline default 15%

Status Trend Risk

Sibling weights shown are MNC baseline defaults — every weight is customer-configurable in the Calibration Lab. The composite runs 0–100 with governed bands — a risk indicator and review priority, never a determination.