False Negative Alarms

Introduction


With the development of financial crimes, money laundering risks pose greater risks for financial institutions. To reduce this risk, there is an AML Compliance Program created by regulators, and financial institutions must comply with these regulations. If they do not, they may be subject to regulatory penalties. It is almost impossible for financial institutions to comply with these regulations with traditional methods because the development of technology has brought technological methods of money laundering.


The technology used by financial institutions to combat money laundering and terrorist financing is AML Transaction Monitoring. With Transaction Monitoring, institutions can scan and report suspicious transactions instantly. Transaction Monitoring Software (TMS) can generate False Positive and False Negative Alarms as a result of these trades. You can find detailed information about False Negative in this article.


What Are False Negative Alarms?


As we mentioned in our blog before, False Positive Alarms are called real non-risk alarms among all suspicious warnings created by Transaction Monitoring, excess of false positives causes loss of time. False Positive is the opposite, False Negative, and the risk and danger of false negatives are much higher. False-negative, money is defined as not noticing the risky transactions to be laundered, the results of this situation are quite bad. While false-positive alerts are a huge waste of time for AML specialists, false negative alerts have far worse consequences such as reputation and large fines. Experts must also deal with false-negative warnings when trying to deal with false-positive warnings because when you solve one, the risk posed by the other may increase.


Alarms caused by user error due to users not being properly trained in system usage. There are also negative false alarms caused by data deficiencies and system deficiencies. The AML industry still relies on traditional techniques and systems to identify information about possible risks. In fact, this is a big mistake to fill the gaps created by traditional methods, financial institutions should use technology compatible AML software.


Why Dangerous False Negative Alarms?


False negatives are uncaught cyber threats in the operation performed and are ignored by security tools software because they are performed immobile and sophisticated. No cybersecurity or data breach prevention technology can eliminate all threats they face. With the development of technology, these crimes are also developing and financial criminals try to deceive these cybersecurity technologies. When False Negative alarms occur in financial institutions, this has certain sanctions. Regulators never accept crimes such as money laundering and terrorist financing in financial institutions. Therefore, organizations must comply with the AML Compliance Program; if False Negative alarms occur, they do not fully comply with this program. As a result, they are fined heavily because they do not comply with regulations, and more importantly, the reputation of their business can be damaged. Considering all this, it can be seen how dangerous the False Negative alarms.


How to Deal with False Negative Alarms?


The best way to scan transactions is to always view the event holistically. All pieces of a puzzle must be identified and taken into account to identify suspicious activity. Thus, False Negative and False Positive alarms can be reduced. Financial institutions can take a holistic look at transactions by implementing machine learning accurately, thus reducing False Negative alarms. Machine Learning can display all activities at once and with all other accounts, capturing the interaction between them, and detecting hidden money laundering activity networks. Transactions in seemingly irrelevant accounts are actually interconnected, and through these accounts, the system can deceive money laundering activities. Therefore, TMS cannot recognize these risks, and False Negative Alarms occur. Machine Learning can see these irrelevant accounts as complementary to identify risks and eliminate them.


To summarize, Machine Learning provides a holistic overview of customer activities and operations. While scanning transactions in TMS, it is important to approach these transactions in a holistic way, so you can find and interfere with the relationships between transactions that seem to be irrelevant by focusing on more than one thing. In this case, you will reduce False Negative Alarms that will endanger your business.











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