Automation Workflow for Control Effectiveness Monitoring and Testing

Manual control testing is a high-cost, low-coverage bottleneck in AML programs. Teams spend weeks pulling logs from Actimize or Oracle Mantas, sampling case files, and compiling findings in spreadsheets. This workflow automates that entire evidence-gathering and evaluation cycle. It schedules tests against key controls—like screening hit resolution rates or SAR filing timeliness—pulls live data from transaction monitoring, case management, and screening systems, and generates findings with attached evidence. The operational upside is a shift from periodic, reactive audits to continuous, proactive assurance, freeing skilled analysts for higher-value risk work while providing real-time visibility into control health.

Implementation integrates with your existing compliance stack. The orchestrator, built on LangGraph or a custom framework, manages the workflow state. Agents extract evidence via APIs or database queries from your core AML systems. Test logic is codified business rules evaluating the evidence. Failures generate findings and remediation tickets in your ITSM, with approval gates and SLA tracking. The architecture includes a dashboard in your Compliance Management System (CMS) for real-time KPI reporting. Critical constraints are data quality validation, exception handling for missing evidence, and maintaining a full audit trail for regulatory examination, ensuring the automation is both effective and defensible.

The workflow automates the testing of key AML controls—like screening hit resolution rates or SAR filing timeliness—by directly querying system logs, case management platforms (e.g., Actimize, Oracle Mantas), and document repositories. It schedules tests based on regulatory cycles or risk triggers, pulls audit evidence, and generates findings reports. This eliminates the labor-intensive, periodic sampling performed by internal audit or compliance teams, converting it into a real-time monitoring layer that provides persistent assurance and dramatically reduces the time-to-discovery for control failures.

Implementation integrates with GRC platforms like ServiceNow or RSA Archer for ticket lifecycle management. The orchestrator, built on frameworks like LangGraph, sequences agents to execute specific test logic, interpret results against policy thresholds, and route exceptions. Critical governance controls include human-in-the-loop validation for all failures, immutable audit logs of test execution and evidence, and rollback capabilities for any automated remediation actions. This architecture shifts compliance from a reactive, audit-centric model to a proactive, data-driven operating system.

Phase 1 establishes the foundational data pipeline and evidence collection agents. We integrate with core systems—transaction monitoring (e.g., Actimize), case management (ServiceNow), and screening logs—to automate the extraction of control performance data. Agents are deployed to pull metrics like screening hit resolution rates and SAR filing timeliness, transforming manual evidence gathering into a scheduled, auditable process. This initial phase delivers immediate visibility and reduces manual data aggregation by 60-80%, proving the workflow's core data utility.

Phase 2 activates the orchestration logic, scheduling tests and generating findings. Using a framework like LangGraph, we build a workflow that triggers periodic control assessments, compares evidence against policy thresholds, and auto-generates findings reports. Human-in-the-loop gates are inserted for high-severity exceptions. Phase 3 integrates remediation tracking with Jira or ServiceNow and deploys executive dashboards. This staged approach allows compliance teams to validate accuracy, adjust logic, and demonstrate concrete time savings—transitioning from sample-based audits to continuous assurance with full operational control.

Manual control testing is a high-effort, low-frequency compliance bottleneck, creating audit risk and delaying remediation. This workflow automates evidence collection from core systems like Actimize, Oracle Mantas, and screening platforms, scheduling tests against defined KPIs such as SAR filing timeliness or screening hit resolution rates. It replaces quarterly sampling with continuous validation, providing real-time visibility into control health and freeing audit teams for higher-value analysis. The operational upside is a 70-80% reduction in manual testing labor and faster identification of control degradation before regulatory findings occur.

Implementation requires integrating with SIEM logs, case management APIs, and document stores to extract structured evidence. A central orchestrator, built with LangGraph or Airflow, manages the test schedule, executes validation logic, and routes failures. Findings generate tickets in ServiceNow or Jira with assigned owners and SLAs, while a dashboard in Power BI or Tableau provides real-time control posture. Critical governance includes immutable audit logs of all test executions, human-in-the-loop approval for significant findings, and rollback capabilities for any logic changes to ensure the testing framework itself remains compliant and defensible.