Why use graphs for compliance?

Networks expose coordinated behavior, shared devices, addresses, beneficiaries that single-record analysis misses, enabling earlier and more decisive action.

Which signals build strong links?

Durable identifiers (devices, funding accounts), transactional edges, and verified relationships (directorships). Weight edges to reflect reliability and recency.

How does it cut alert noise?

Grouping related alerts into cases reveals the bigger picture and avoids duplicate work, improving precision and analyst throughput.

Over-linking from weak signals. Apply thresholds, provenance tracking, and reviewer guidelines to maintain accuracy and fairness.

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Link Analysis

Overview

Link analysis maps relationships among customers, accounts, devices, merchants, and transactions to reveal hidden risk structures such as mule networks, fraud rings, and sanctioned party proximity. Nodes represent entities; edges capture shared attributes (IP, device, address), funds flows, or corporate ties.

Graph metrics (centrality, community detection) surface hubs, clusters, and bridging nodes that traditional row-by-row reviews miss. In AML/fraud programs, link analysis powers alert grouping, prioritization, and network-level interventions (coordinated closures, escalations to law enforcement).

Effective deployments integrate entity resolution, sanctions/PEP hits, and jurisdiction risk to give analysts context-rich views. Governance requires data minimization, lawful purposes, and documented lineage. Regular playbooks help analysts interpret patterns consistently, while model feedback loops refine rules.

Link analysis materially improves interdiction speed, reduces false positives, and produces stronger, more coherent SAR narratives.

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