How Verafye Fits
Connected Intelligence Alongside Existing Monitoring
Most commonYour existing TM system stays in place. Verafye connects alongside it and adds entity graph, network context, device and behavioural signals to each alert — improving investigation quality without changing your monitoring programme.
Best for: Institutions with existing TM investment seeking stronger connected-risk context around each suspicion
Verafye-led Monitoring
Run transaction monitoring natively within Verafye — rules engine, AI-based detection, typology patterns, and behavioural analytics in one platform, from alert creation through case closure.
Best for: Institutions building a new monitoring programme or replacing a legacy stack
Parallel Assessment
How RSR startsRun Verafye alongside your existing monitoring on actual transaction data — without any commitment to change. Risk Shadowing produces a direct comparison to determine whether connected context adds material incremental intelligence.
Best for: Institutions evaluating monitoring coverage or considering modernisation
The Problem
Transaction monitoring is one of the highest-cost, lowest-signal functions in financial crime operations for many institutions. Legacy monitoring stacks generate large alert volumes, yet many of those alerts do not lead to substantive investigations — consuming analyst capacity without proportionate improvement in risk detection or compliance outcomes. Transaction monitoring is also a non-discretionary AML obligation: regulators expect institutions to demonstrate effective controls, proportionate coverage, and the documented decision trails that examiners scrutinise during review.
Why Legacy Falls Short
Rules engines fire on individual transaction attributes - amount thresholds, velocity patterns, geographic triggers - without access to the entity, relationship, or behavioural context that would distinguish genuine risk from routine activity. The result is persistently high false positive rates that erode analyst trust and inflate operational cost.
Transaction monitoring systems evaluate payments in isolation - without awareness of the entity behind the transaction, the device used, the behavioural history, or the network of related accounts. This absence of context means alerts arrive without the information analysts need to assess them meaningfully.
Alert generation and case investigation remain disconnected processes in most monitoring stacks. Analysts receive alerts with no structured investigation context - requiring manual platform switching, data retrieval, and case assembly before any substantive review can begin.
As transaction volumes grow, alert volumes scale proportionally under rules-based monitoring - and the typical response is adding analyst capacity. This model does not inherently improve detection quality, alert signal quality, or investigation speed as the institution scales.
Before vs After
Without Verafye
Alerts generated on transaction attributes alone - no entity or network context
High false positive rates - analysts spend most of their time ruling out noise
Fraud and AML monitoring queues are separate - cross-domain risk invisible and compliance gaps harder to evidence
Analysts manually switch platforms to gather case context before investigation begins
Alert volumes grow with transaction volumes - headcount scales with cost, not intelligence
With Verafye
Alerts enriched with entity profile, device signals, and network context at generation
Contextual enrichment helps deprioritise legitimate activity earlier - fewer false positives reaching analysts
Connected fraud and AML monitoring view - cross-domain risk visible in a single queue, with documented decision trails that support regulatory review
Pre-assembled case context delivered at alert creation - analysts investigate, not research
Connected entity and relationship context can help investigators evaluate coordinated behaviour that may not be apparent when alerts are reviewed individually.
How Verafye Improves It
Verafye connects transaction signals with entity profiles, behavioural patterns, and network relationships - so alerts arrive enriched with the context analysts need to assess, prioritise, and investigate without manual reconstruction.
Transaction alerts are enriched with the entity profile behind them - account history, device signals, identity attributes, and behavioural patterns - giving analysts meaningful context at the moment an alert is surfaced rather than after manual research.
Graph traversal connects transaction patterns across related accounts, devices, and entities - surfacing coordinated activity, network-level risk, and relationship context that transaction-level monitoring does not surface on its own.
Alerts are organized by entity context, network risk, and cross-system signals - improving investigation queue ordering based on risk relevance rather than transaction volume, recency, or alert age alone.
Contextual enrichment at the alert stage supports earlier identification of lower-priority activity in the workflow - improving alert signal quality and reducing the analyst fatigue and operational cost that volume-based queues create.
Verafye connects transaction monitoring directly to investigation workflows - delivering alerts as structured, context-rich cases with pre-assembled evidence, enabling analysts to move from alert to investigation decision without manual context gathering, and preserving audit-ready evidence trails for compliance review.
Transaction signals from fraud and AML monitoring are connected into one network view - closing the visibility gaps that form at the boundary between fraud detection and AML transaction monitoring and enabling cross-domain risk assessment.
Key Capabilities
Bring transaction alerts together with fraud, AML, identity, device, behavior, and third-party enrichment signals - feeding normalized alert context into review queues and case formation workflows.
Explore PlatformGroup related transaction activity into investigation-ready cases instead of isolated alerts - routing clustered cases into structured review queues with pre-assembled entity and transaction-to-entity linkage context.
Explore Investigation IntelligenceReveal relationships across accounts, users, merchants, counterparties, devices, and payment flows.
Explore Graph IntelligencePreserve case notes, linked evidence, decision rationale, analyst actions, and reviewer controls - maintaining structured evidence trails and audit-ready investigation records for compliance oversight.
Explore Investigation IntelligenceBusiness Impact
Contextual enrichment at the alert stage enables legitimate activity to be identified and deprioritised earlier - improving alert signal quality and reducing the proportion of non-actionable alerts that inflate investigation workload and erode analyst confidence in the monitoring system.
Graph intelligence and cross-system signal aggregation improve the quality of monitoring outputs - surfacing alerts that carry genuine risk indicators rather than triggering on transaction attributes alone, improving the ratio of actionable to non-actionable alerts.
Pre-assembled case context and direct integration between monitoring and investigation workflows eliminate the manual research phase - enabling analysts to move from alert to an investigation-ready decision with greater confidence and a clear audit trail.
Better alert prioritisation, improved signal quality, and structured investigation workflows reduce the per-alert workload - enabling institutions to manage growing transaction volumes without proportional increases in analyst headcount.
One connected network view linking transaction monitoring to entity profiles, network relationships, and cross-system signals gives fraud and AML teams a connected, contextual view of risk - enabling better-informed decisions across monitoring, investigation, and reporting functions. This connected view also supports the explainability and audit trail requirements that regulators expect from institutions operating transaction monitoring programmes.
One connected network view linking transaction monitoring to entity profiles, network relationships, and cross-system signals closes the gap between alert generation and investigation context - enabling fraud and AML teams to act on a connected, cross-system view of risk across the monitoring programme.
Relevant Industries
See how transaction monitoring workflows can feed alert clustering, review queues, case formation, and evidence trails - giving risk teams clearer, evidence-grade investigations from the signals monitoring already generates.
No platform-replacement commitment required. Speak directly with our solutions team.
