Transaction Monitoring is an essential component of an effective anti-money laundering (#AML) program, helping financial institutions detect and prevent illicit activities.
However, with increasing volumes of transactions and data, it can be challenging for institutions to monitor effectively.
That’s why it’s crucial to implement best practices for transaction monitoring which include:
Conduct risk-based transaction monitoring
- Based on the level of risk posed by a customer or transactions
- Set thresholds for activities and transactions that require investigations
Use Advanced Analytics and Artificial Intelligence (AI)
- To analyze large volumes of data and generate alerts for potential suspicious transactions
- Consider machine learning models to improve the accuracy of transaction monitoring and reduce false positives.
Monitor Both, Structured and Unstructured Data
- Structured data includes information already held with the company i.e. customer information, transaction data and history
- Unstructured data involves social media activity, correspondence, voice recordings etc.
Ensure sufficient resources
- Enough staff to handle transaction monitoring and alerts
- Adequate training to staff to the latest money laundering trends and techniques.
Conduct adequate testing
- Such as audits and reviews to identify areas for improvement and ensure compliance with regulatory requirements.
What other best practices do you recommend for transaction monitoring?
