GFN logoGFNGlobal FinCrime Network
Subscribe

GFN Visual Series

How the Transaction Monitoring (TM) Cycle Works

A clear, end-to-end breakdown of how modern Transaction Monitoring operates as a continuous feedback loop — from data ingestion and detection to investigations, reporting, and model tuning.

Click the image to view it in full resolution.

How the Transaction Monitoring (TM) Cycle Works

Transaction Monitoring (TM) is not a single model, rule, or alerting system.
It is a continuous, end-to-end cycle designed to detect, assess, and respond to suspicious financial activity over time.

A modern TM framework operates as a closed feedback loop — where data, risk, detection logic, investigations, and reporting continuously inform and refine each other.

This visual illustrates the full Transaction Monitoring cycle, followed by a detailed explanation of each step.

Step 1 — Data Ingestion & Normalization

Everything starts with data.

Objective:
Ensure all relevant transactional and contextual data is captured, standardized, and usable.

Key Inputs

  • Transaction data (payments, wires, ACH, cards, crypto, internal transfers)
  • Customer profiles and KYC data
  • Account and product information
  • Counterparty data
  • Channel and device metadata
  • External enrichment (geolocation, IPs, merchant data)

Why it matters:
Poor data quality creates blind spots, drives false positives, and leads to missed risk.
Transaction Monitoring can only be as strong as the data feeding it.

Step 2 — Customer & Behavioral Baseline

TM does not evaluate transactions in isolation.

Objective:
Understand what “normal” looks like for each customer or entity.

Core Components

  • Expected activity profiles based on customer risk, product, and geography
  • Historical transaction behavior
  • Peer group comparisons
  • Velocity and volume patterns

Why it matters:
Suspicion is defined by deviation from expected behavior, not by transaction size alone.

Step 3 — Detection Logic (Rules & Models)

This is the detection engine of the TM program.

Objective:
Identify transactions or behavioral patterns that may indicate suspicious activity.

Detection Layers

  • Rule-based scenarios
    Threshold breaches, velocity spikes, unusual patterns

  • Behavioral and statistical models
    Detection of anomalies and subtle risk signals

  • Machine learning models
    Identification of complex, non-linear behaviors across time and entities

  • Network and relationship analysis
    Detection of hidden connections between customers, accounts, and counterparties

Why it matters:
Effective Transaction Monitoring relies on multiple detection layers, not a single model or rule set.

Step 4 — Alert Generation & Prioritization

Not every signal deserves the same level of attention.

Objective:
Convert detection signals into actionable alerts and prioritize analyst effort.

Core Components

  • Alert generation based on detection thresholds
  • Risk scoring at alert or case level
  • Suppression of low-risk or redundant alerts
  • Aggregation of related signals

Why it matters:
Without prioritization, TM systems overwhelm analysts, reduce effectiveness, and increase operational risk.

Step 5 — Alert Review & Investigation

Human judgment enters the loop.

Objective:
Determine whether the flagged activity is genuinely suspicious.

Investigation Process

  • Review transaction history and timelines
  • Assess customer profile and expected behavior
  • Analyze counterparties and transaction flows
  • Gather contextual and external information
  • Build a coherent narrative of activity

Why it matters:
TM systems surface signals — analysts determine suspicion.

Step 6 — Case Management & Decisioning

Alerts rarely exist in isolation.

Objective:
Consolidate related alerts and reach a clear, defensible outcome.

Possible Outcomes

  • Close as false positive
  • Continue monitoring
  • Escalate customer risk level
  • Apply restrictions
  • Exit the relationship

Why it matters:
Consistent decisioning ensures defensibility, auditability, and regulatory alignment.

Step 7 — SAR/STR Filing (When Required)

If suspicion remains, institutions must report it.

Objective:
Provide actionable financial intelligence to regulators and law enforcement.

Reporting Elements

  • Clear, factual narrative
  • Description of suspicious behavior
  • Supporting transaction evidence
  • Timely submission based on jurisdiction

Why it matters:
Transaction Monitoring is a detection system — SAR/STR filing is its regulatory output.

Step 8 — Feedback Loop & Model Tuning

The TM cycle never ends.

Objective:
Improve detection accuracy and reduce noise over time.

Feedback Sources

  • Investigation outcomes
  • SAR/STR quality reviews
  • Regulatory feedback
  • False positive analysis
  • Emerging typologies

Actions

  • Rule and threshold tuning
  • Model retraining
  • Scenario enhancement
  • Risk parameter adjustments

Why it matters:
A static TM system degrades quickly as criminal behavior, products, and channels evolve.

Putting It All Together — TM as a Continuous Cycle

Transaction Monitoring is not linear.
It is a closed-loop system:

  • Data feeds detection
  • Detection generates alerts
  • Alerts trigger investigations
  • Investigations drive decisions
  • Decisions inform reporting
  • Outcomes refine models and rules

The effectiveness of a TM program depends not on any single step, but on how seamlessly the entire cycle operates end to end.

Note on AI-Assisted Visual Creation

Our infographics use AI in the creation process, and we continuously refine our visuals.
If you notice anything that can be improved or clarified, please let us know — your feedback helps strengthen the FinCrime community.

This infographic was created with the support of AI. We continuously refine our visuals — if you notice anything that can be improved, please let us know.