Regulatory compliance is an important and mandatory obligation for banks and financial institutions. They are required to develop and implement a risk-based approach to prevent money laundering and terrorism funding at all levels of their operations. Through this risk-based approach, Red Flags are raised which is a systematic process. These red flags alert the compliance team of any suspicious activity that may be a money laundering activity. But that’s not always the case.
A False Positive in AML is just like a false alarm where the actual Money Laundering was not taking place. While raising suspicion for money laundering, AML False Positive reduction is a complex but necessary step of every compliance procedure. It requires different steps and approaches to mitigate any false alerts for better customer care and security. Since false positives can compromise customer satisfaction and trust in the system, they can also pose a threat to customers’ dignity where a business might permanently lose a customer for falsely pointing a finger at an individual.
Following are a few strategies that can help in AML False Positive Reduction.
Improving the AML System:
Anti-Money Laundering system always generates Red Flags on risk factors that include the transacted amount, sources of funds, location and etc. By improving these parameters financial institutions can reduce false positives. For example, limiting the geo-location, setting a higher amount of transactions, and limiting the number of alerts for a single customer will reduce false alerts.
Machine Learning and AI
Implementing Machine Learning and Artificial Intelligence is helping industries to automate almost every system and remove errors in it. Similarly, False Positive AML Alerts can be minimized with the use of Machine Learning and AI software in conducting KYC and AML procedures. Since both AI and machine learning create patterns of previously flagged suspicious transactions, it is less likely that the AML system will repeat an error once corrected.
Enhanced Risk-Based Approach
A Risk-based approach will assess the level of risk associated with customers and their transactions. For high-risk customers, the approach can become stringent and in this way, the accuracy of the AML system can be improved.
Improved Data Structuring
Poor quality of data and data sorting can also lead to an increased number of False Positives in AML. Therefore, data accuracy, data completeness, and up-to-date data are important aspects of Data Structuring to remove any discrepancies and make the AML system error-free. In this way, the reduction of False Positives is ensured.
Enhanced Ongoing Monitoring
Ongoing Monitoring and human oversight can also help AML False Positive Reduction. Sometimes the system is not efficient enough to fulfill all the AML requirements. There a human eye is needed to monitor the system’s activities and cover up for the loopholes in the system. If the system incorrectly raises False Positives, the AML analysts can review the alerts and make decisions based on experience and expertise.
Conclusion
Reducing False Positives in AML is crucially important for a successful compliance program. False Positives always result in the wastage of resources, reduced system efficiency, and increased compliance costs. Ultimately, the system might not achieve its compliance objectives and be discarded or replaced. By adopting the above-discussed strategies, one can ensure improvement in the system and how it should analyze a risk properly where the thin line between a customer and a suspicious person must not be crossed. Customer trust is one of the most important aspects of a successful KYC and AML program. So, reducing False Positives is mandatory and an ongoing effort for AML solutions that are risk-free.
