AML compliance teams are facing growing challenges in how they handle backlogs of alerts. Traditionally, banks and financial institutions have approached these backlogs by reviewing alerts in the order they were created.
According to Consilient, this process, rooted in outdated workflows, focuses on throughput rather than the actual severity of risk. However, regulators are less concerned with how many alerts have been cleared and far more focused on whether serious risks are being overlooked due to inefficient review methods.
This creates a difficult situation for compliance leaders, who are responsible for ensuring high-risk cases are escalated promptly. Many current systems do not offer a practical way to evaluate exposure within the backlog. Without a clear logic for determining which cases are reviewed first, institutions risk regulatory scrutiny and potentially severe consequences.
The blog introduces ranked case review as an alternative approach, which prioritises older alerts based on transaction-level risk rather than when they were raised. By applying alert risk identification, institutions can surface the most critical cases, address hidden exposures, and build an audit trail to support their decisions.
Chronological case reviews often create a false sense of progress. Investigations usually proceed on a first-in, first-out basis, but this means low-risk alerts are often examined ahead of higher-risk ones simply because they arrived earlier. This approach can result in delays of several weeks between an initial alert and the filing of a suspicious activity report (SAR), violating regulatory expectations.
The rules are clear: SARs must be filed no later than 30 calendar days after detecting facts that justify a report, with limited extensions. Regulators want assurance that case reviews are driven by actual risk rather than outdated operational targets. Without this, there is a growing disconnect between investigative work and the potential harm being missed.
Ranked risk scoring offers a way to restructure this process without overhauling existing investigation workflows. Instead of following a rigid queue, teams can triage backlogs based on exposure. Transaction risk scoring assigns severity scores to each alert, using behavioural indicators learned from patterns across multiple financial institutions. These models are designed to recognise serious financial crime patterns based on real-world data.
Importantly, the Federal Financial Institutions Examination Council (FFIEC) emphasises that staffing levels must be sufficient to investigate alerts without tailoring the volume of investigations simply to fit resources. Without intelligent prioritisation, staffing costs rise, management struggles to coordinate teams, and investigators face burnout from reviewing repetitive, low-value alerts.
Transaction risk scoring provides structure to these issues. Backlogs are no longer flat queues but ranked according to risk severity. The riskiest alerts surface first, even if they were generated after lower-risk cases. This approach requires no disruption to existing systems, as the scoring model works independently of core detection engines.
Crucially, this system builds explainability into every score. Each case’s risk level can be justified with a transparent rationale, providing the audit trails necessary for internal governance and regulatory inspections.
Read the full RegTech Analyst post here.
Keep up with all the latest RegTech news here.
Copyright © 2025 RegTech Analyst
