A financial institution recently purchased anti-money laundering software.

The individual responsible for setting the anti-money laundering software parameters should recommend ensuring that the alert parameters are based on risk, rather than on default software rules. This means that the software should be configured to generate alerts only when transactions or customers exhibit a high level of risk, based on factors such as the source and destination of funds, the amount and frequency of transactions, the type and purpose of the account, the customer profile and behavior, and the presence of any red flags or indicators of money laundering or terrorist financing. By aligning the alert parameters with the risk assessment of the financial institution, the software can reduce the number of false positives and focus on the most relevant and suspicious cases, thus improving the efficiency and effectiveness of the anti-money laundering staff.

Setting AML Transaction Monitoring Thresholds 1, ComplyAdvantage, 2020.

Machine learning techniques for anti-money laundering (AML) analysis: a survey 2, Springer, 2018.

Anti Money Laundering (AML) Definition: Its History and How It Works 3, Investopedia, 2021.