The Problem
Traditional monitoring evaluates events in isolation. Modern fraud, mule activity, synthetic identity abuse, and layered AML typologies operate across connected entities, devices, accounts, and behaviors — deliberately structured to stay below the thresholds that rule-based systems monitor. The result is fragmented detection, incomplete investigations, and growing pressure to demonstrate the cross-system visibility that regulators increasingly expect.
Why Legacy Stacks Fall Short
Point-in-time scoring evaluates individual events without awareness of the network connecting them. Coordinated schemes deliberately stay below individual thresholds — visible only when signals are connected across entities and time.
Fraud and AML teams operate on separate platforms with separate alert queues. Cross-domain connections remain invisible — and neither team sees the full picture of risk the data already contains.
Analysts review individual alerts with no access to entity relationships or network structure. Each case requires manual research to surface connections that a graph layer would surface automatically.
Each individual transaction or account within a fraud network may score low risk on its own. Only when viewed as a connected structure does the coordinated scheme become visible — and only graph traversal can surface it.
How Verafye Solves It
Verafye connects fraud, AML, and payments signals into a unified intelligence layer — resolving entities, mapping relationships, and surfacing network risk in real time. This gives institutions the connected view of risk that fragmented monitoring cannot provide, and the traceable detection that aligns with evolving regulatory expectations for cross-domain visibility.
Verafye resolves identities across fragmented data sources — linking accounts, devices, phone numbers, addresses, and behavioral fingerprints into unified entity profiles.
Every resolved entity is connected to others through shared attributes and transaction history, building a living graph of relationships across your institution.
Verafye continuously traverses the graph to surface non-obvious links — connections that are invisible to rules engines and siloed monitoring systems.
Connected entities are grouped into clusters — revealing fraud rings, mule networks, and synthetic identity cohorts operating across accounts and payment rails.
Every alert is enriched with relationship context from the graph, giving investigators the network view they need to make faster, higher-confidence decisions.
Fraud, AML, and payments signals are connected into a single intelligence layer — eliminating the blind spots that form at system boundaries, and supporting the cross-domain visibility that institutions need to operate under evolving regulatory expectations.
Core Capabilities
Resolve and deduplicate identities across accounts, devices, and data sources into unified entity profiles — eliminating the fragmentation that allows coordinated activity to go undetected.
Build and continuously update a living graph of relationships across entities, transactions, devices, and behaviors.
Surface non-obvious links and hidden network structures that are invisible to transaction-level monitoring and rules-based detection.
Score risk at the network level — not just the transaction level — incorporating relationship depth, cluster size, and cross-system signal strength.
Group related alerts from across fraud, AML, and payments into consolidated investigation clusters — reducing noise and investigation workload.
Deliver graph-enriched investigation context directly alongside every alert — so analysts see relationships, not just events.
Business Impact
Graph traversal surfaces coordinated schemes before they accumulate losses — connecting signals across accounts, devices, and time windows that point-in-time scoring misses.
Alerts enriched with graph context allow investigators to prioritize by network risk — focusing effort on the highest-impact clusters first.
Alert clustering and graph-enriched context reduce the time analysts spend on manual research — consolidating related alerts into prioritized investigation queues.
Graph clustering reveals mule account networks and synthetic identity cohorts that share attributes, devices, and behavioral patterns across your portfolio.
Unified fraud and AML signal intelligence eliminates blind spots at system boundaries — giving compliance and operations teams a complete picture of risk across the institution.
Pre-built relationship context and network clusters reduce the time from alert generation to meaningful investigation — compressing triage cycles across fraud and AML operations.
Built For
Talk to our team about how Verafye surfaces hidden financial crime networks across your fraud, AML, and payments data — helping institutions operate within evolving regulatory frameworks.
No commitment required. Speak directly with our solutions team.
