False Positive Reduction in AML Transaction Monitoring

False Positive Reduction in AML Transaction Monitoring

Transaction monitoring and screening are one of the essential steps of a successful anti-money laundering (AML) process. It is fully under the responsibility of the financial institutions to detect unusual or suspicious transactions, therefore they need to ensure that they employ a sufficient transaction monitoring and sanction screening system. The regulatory pressure for ensuring the most secure financial experience increase the operational burden tremendously. The amount of and unstructured nature of data in payment and reporting processes causes problems of misinterpretation of data and compliance screening which in turn increase the number of false positives and finally results in suspensions in the transaction processes. As a result, reducing false positives in transaction monitoring especially for AML is an important solution for the financial institutions to improve their efficiency.

Why and when does a false positive occur?

A false positive is when an innocent, legitimate transaction is defined as suspicious during the monitoring and screening processes, so the transaction is identified as suspicious and risky incorrectly. The transaction monitoring systems has parameters installed to detect suspicious or risky behavior and create alarms accordingly. When the monitoring system generates an alarm for a transaction that is actually unsuspicious, it means it has created a false positive. As these systems are very sensitive, they can generate false positives in each risky or unusual financial activity. 40% of consumers stated that they have received alerts from banks that their transaction is flagged as suspicious as it turns out that they are actually legal.

How to reduce false positives?

Financial institutions can benefit from the advanced technology solutions against the regulatory and operational challenges of the digitalized financial world. The recent developments in the field of technology offer more automated and intelligent methods by combining advanced data analytics (for fighting risky transactions) and better compliance experience. Artificial intelligence (AI), machine learning (ML) and other advanced algorithms can be employed in the transaction monitoring and detecting risks in financial activities for the sake of improving the overall quality of the financial transactions and compliance with the Anti-Money Laundering (AML) regulations. The benefits of using AI and machine learning in transaction monitoring are as follows:

  • Embracing big and real-time data: Fraudulent and illegal activity has reached its peak in the digital eco-system. Real-time analysis and processing of large quantities of data and categorizing them based on the level of risk thanks to the AI and Machine Learning technologies, help financial institutions tremendously in decreasing the number of inaccurate alerts. This also automatically reduces the possibility of mistakes resulted by human errors.
  • Adaptive and learning technology: The learning nature of AI and Machine Learning technologies enables understanding and anticipating user behavior. Eventually, they can detect suspicious activities without defining scenarios, this is called intelligent detection prioritizing. They keep adding and rationalizing the existing and new data, as a result, the false positives can be reduced to a great extent and crime detection rates can arise tremendously.