The emergence of New Technologies in the payment space, open banking and cryptocurrencies have all paved the way for the need for new technology innovation in the monitoring of transactions.

The need to upgrade to new technologies such as AI and Machine Learning for the monitoring of transactions to efficiently mitigate the organization’s AML risk remains a top priority for financial Institutions (FIs). The investment in new technologies used within the anti-money laundering (AML) space have been increasing year on year by FIs.

Regulators expect firms to be able to show not only that they have a system in place to monitor transactions, but they must also be able to prove that it is effective. The regulations have created a shift from quantity of alerts being generated to quality of alerts generated from the system. This is a significant move away from the approach of looking at technology to merely reduce the number of alerts.

Whether an organizations is planning on developing and implementing an In-House AML Transaction Monitoring (TM) System or whether they are planning to purchase vendor sourced AML Technologies, the below factors must be considered:

Understanding Data: A critical part of TM is the data volume and data quality.

  • Data volume should flag if the system being implemented would be capable of handling the data volumes. The system also needs to be validated to understand any bottlenecks which may arise if there are significant changes in volumes.

  • Data Quality is an important part of transaction monitoring since feeding the wrong information or missing information may create inaccurate results; For Example- A Scenario created to monitor transactions for Politically Exposed Persons (PEP) customers where the data element representing the PEP Flag has not been loaded would not generate an alert in this case.

  • Data Sources – The AML TM system must be capable of accepting information from various source systems. It is important to implement a system which can operate with different data formats. The people who implement the system should analyse the different source systems within the FI and be capable of extracting information which is necessary from different data sources and create a metadata extract from the consolidated information.

Technology Infrastructure –A lot of cost is involved in the setup and operation of a monitoring solution; hence, it is important that the solution should be able to interact and operate seamlessly with the existing infrastructure of the FI. The AML TM system should be able to connect to other systems such as KYC system and product processors to obtain data and for providing an enterprise wide risk coverage.

Scenario Selection: A FI should focus in choosing the right scenarios which would help to mitigate the risks from the products & transactions. The scenarios selected should cover the common red flags of money laundering and other financial crimes, such as fraud, for that specific product. The scenarios selected should be documented and discussed with the vendor and the expected outcome of the scenarios should be clearly described to the vendor.

Threshold setting & Tuning: The initial threshold setting for the scenarios selected is a vital step when implementing an AML TM system. This step should help to prove that the monitoring is more focussed on the quality of alerts generated by the system. The initial thresholds are set after analysing the data in detail.