Six Steps to Analyzing an AML Program’s Efficiency and Effectiveness


ACA Compliance Group

Publish Date



  • AML and Financial Crime

Every anti-money laundering (AML) program should be reviewed periodically to confirm that the program is performing efficiently and effectively. Analytics can play a big role in this review by providing new insights that support evidence-based decision-making.

For example, being able to provide senior management, and the board, with detailed data about the efficiency of a compliance program can help underpin an argument for more resources to manage the additional work that a new line of business will bring. Alternatively, such anti-money laundering program analysis can give stakeholders assurance that the AML program’s technology solutions are working as they should. There are six key methods financial institutions should utilize to ensure a robust approach to AML program analysis:

  1. Model Validation – Today, model validation is essential for AML, Bank Secrecy Act (BSA), and Office of Foreign Asset Control (OFAC) systems compliance programs. Not only is it a AML program regulatory requirement, it is also best practice for firms seeking to proactively manage their risks. Analytics can help firms detect problems with anti-money laundering program models quickly so they can be fixed and poor-quality decisions are not taken on the basis of incorrect output. A model validation review can help ensure that models are up to date, the logic of the models is still current, and that the model is complete in its structure. It also gives assurance that the firm is compliant.
  2. Data Profiling – Data profiling is the process of analyzing an existing data source to provide statistics and information about the data. Data profiling enables firms to demonstrate that they have a proper understanding of the source of data for risk ratings and transaction monitoring. Firms need this understanding of the existing data they have within their technology systems to be sure they are correctly monitoring transactions for suspicious activity. For example, if the content of an important data stream is not understood, it could mean that some transaction flows are not being monitored correctly, or at all within AML transaction monitoring solutions.
  3. Data Quality – Data quality is very important for achieving the correct AML transaction monitoring outcomes. Poor quality data can lead to a high rate of false positives or else missed criminal activity. Data quality checks should include a review of selected samples of source-to-target data maps, paying particular attention to fields that can be troublesome. Data within the technology system of origin should be compared with the data as it appears within the AML solution that is using it to be sure it is being communicated correctly. In addition, anti-money laundering program reviewers should look for empty data fields and data fields that are not being populated correctly. For example, in one case, empty data fields were populated automatically with “NA” to indicate “not applicable” at the data source. However, the AML transaction solution was reading these entries as “Namibia”, and generating many false positives as a result. Automated dashboards can help compliance teams monitor data quality on an ongoing basis.
  4. ATL/BTL Testing – One way of tuning AML transaction monitoring models is through applying statistical methods known as above-the-line (ATL) and below-the-line (BTL) testing. These approaches are used to validate and tune the thresholds and parameters of the rules in the software. To do ATL or BTL testing, the thresholds are increased or decreased in order to try and arrive at the best possible thresholds and parameters. These thresholds are adjusted in the software’s testing environment and then alerts are generated for a period of time, for example, the previous six months. Ideally, ATL and BTL testing should not just be performed once, but periodically to ensure that the model is correctly tuned. The nature of transaction data can evolve with changes in the firm’s business.
  5. Threshold Analysis – This is a “what-if” analysis based on the thresholds within the anti-money laundering program models. This approach can be used to fine tune the models and enhance their effectiveness. For example, if the compliance policy currently states that case investigations need to be finished within 20 days, a new policy can be modelled in which all case investigations need to be finished in 15 days. The model will show how much of a backlog will build up using current levels of resources with the new timeframe. This kind of modelling can aid decision-making in a wide number of ways within an AML program.
  6. Capacity Planning – This in-depth analysis focuses on operational aspects of a firm’s AML program. For example, it can help identify which AML rules are costing the most in time and resources or which analysts are the most efficient in processing cases. Capacity planning analysis can help to ensure that an AML team is working at its best, reducing costs and increasing their effectiveness.


In short, these six approaches can help compliance teams acquire the insight needed for evidence-based decision-making about resources, costs, and time spent on AML-related activities. Sharing information from such anti-money laundering program analyses with internal stakeholders can aid in building business cases as well as affirming the effectiveness of existing compliance programs. These kinds of analytics, which are similar to those that the lines of business themselves use in many organizations, can help ensure the voice of the AML team is heard.