Top 5 measures for AML transaction monitoring systems


Active or real time monitoring of financial transactions should be the order of the day for institutions and businesses handling high volumes of deposits and withdrawals. This capability is essential in mitigating risks, thereby improving the monitoring and detection process. By effectively and actively monitoring transactions, organizations are in a better position to meet their AML obligations without impacting their day-to-day operational processes.

Impediments to real time transaction monitoring

  • Data quality and mapping: consolidation of transactions on different platforms is perhaps the most challenging aspect of AML. This is because the data needs to be coherent for monitoring purposes. Data quality needs to be taken into account, especially since sometimes, similar batches of data are grouped together, making it difficult to monitor holistically.

  • Lack of feedback on SARs: an organization expects that whenever a suspicious activity report (SAR) is filed, that it receives feedback from the competent authorities about the investigation and the outcome. However, sometimes, this feedback is not up to standard or even non-existent to allow the self-improvement of the organization’s monitoring capabilities. This leads to improvement efforts being deemed subjective as people within the organization itself have to come to their own conclusion on what is working and what is not.

  • Scenario alerting techniques: if an organization keeps moving the goalposts as to what is considered risky and what is not, quality of monitoring will be diminished. Non-transparent analytical methods such as neural networks add another complex layer in relation to the unpicking and explanations for decisions.

5 measures for accurate transaction monitoring

  • SAR disclosure rates: by categorising the files of cases being submitted, understanding the reason behind the majority of submitted cases, and acknowledging the areas of business affected – an organization can extract valuable quantitative information which can be investigated.

  • False positive ratio: comparing the overall changes in this measure provides an indication of the monitoring system’s performance. When at high-level, the ratio provides the best indications of any mismatch between detection processes and overall risk.

  • Alert volumes: any changes in alerting volumes should be investigated. Some potential issues could be underlying within the transaction monitoring system itself. Some examples include scenario changes, rule changes, setting up of new scenarios, introduction of new business lines, and the introduction of new types of transactions.

  • Operational costs: costs also need to be monitored in two aspects. The first aspect is the size and capability of the investigative team which could also be handling day-to-day system tasks. Secondly, the supporting infrastructure around the team needs to be constantly assessed as well. In this case, operations growth needs to be taken into account.

  • Number of monitoring rules: as the number of scenarios or rules increases, so does the strain on system management processes which in turn could lead to a higher number of cases. An increased number of rules could be due to changes to existing compliance regulations, lack of consolidation in account segmentation, and new products or business lines being added.

The Solution

ComplyRadar helps you address AML transaction monitoring requirements by automatically identifying suspicious behaviour in real time or on a scheduled basis, while minimising false positives. Get in touch with us today to book your free, no-obligation demo. Visit for more information.

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