Having a correctly tuned AML solution is essential for any organization dealing with high volumes of financial transactions. With compliance risks being higher nowadays, regulators are constantly inspecting AML transaction monitoring systems to ensure adherence to international regulations.
One of the main issues faced is the high number of false positives which can be caused by an incorrectly tuned transaction monitoring system. Then there is the other side of the coin – if an organization performs extensive tuning on its AML software, it runs the risk of reducing the effectiveness of the system. If caught by authorities this lack of effective monitoring could also result in fines.
Model and data alignment
The first step for any tuning exercise should be to understand the objectives of the model. Is it risk minimisation or detection of suspicious behaviour? The second step would be to identify the data feed that will be used for the model – meaning the type of data the model will be using to monitor transactions. The data type needs to satisfy the requirements of the model. For example, it is no use having a model which is able to monitor online bank deposits if the data that will be provided to the model will not include such information.
Therefore, organizations and institutions need to establish their main objectives when it comes to compliance so that their mitigation expectations are met via the model and data type they will be using. With these criteria established, it will be easier to tune the software according to their specific needs.
5 common tuning errors
Data and model conflict: when elements in the data field are misused and misread by the model, errors are made resulting in false positives. For example, if a ‘country’ field is left empty and filled in as ‘NA’ which stands for not applicable, the model may misinterpret it as being Namibia. Therefore, for the model, all transactions with an empty ‘country’ field will have originated from Namibia.
Structuring patterns: criminals use the structuring process to deposit illegal funds into accounts by breaking down the large amount of funds into a number of smaller transactions. These transactions are created in a way which fall below the reporting threshold in an attempt to avoid detection. However, honest businessmen also make use of this tactic on a frequent basis and therefore such transactions can be wrongly flagged. Therefore, the software needs to be tuned to catch suspicious activity via customer profiles and the pattern of transactions.
Velocity patterns: velocity patterns are noticed when a customer receives a deposit in their account and then rapidly withdraws the funds a few minutes later. This can be a sign of suspicious activity, although it can also represent a genuine client withdrawing money to pay their bills. Tuning the software to recognise certain patterns is essential for removing false positives.
Exclusion lists: these exclusions or ‘whitelists’ are created by organizations to exempt certain customers from being monitored by the AML software. This is usually done to minimise false positives given the customers’ reliable track record of legitimate transactions and honourable business practices. If, however, these lists are not reviewed periodically and patterns studied for abnormal behaviour, this limited amount of tuning could lead the organization into hot water with the relevant authorities. Reason being that without performing risk assessments on whitelisted clients, illegitimate transactions by usually reliable people can go under the radar, resulting in no flags being raised.
Suppressions: organizations sometimes tune their software to suppress alerts based on particular criteria in order to minimise false positives. They have to ensure however, that the system is properly tuned so that illegitimate transaction alerts are not suppressed in the future. If such correct tuning is not in place, an analyst may end up classifying an alert as a false positive given that they are unaware of the subsequent alerts which the software had suppressed.
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