Checking for fraudulent or suspicious transactions is part of any financial institution’s duties. With money-laundering schemes and scams a-plenty, it always helps to be thorough and advanced with one’s methods for dealing with them. This is also why many banks have decided to automate their transaction monitoring solutions. The sophisticated technology and rule-based artificial intelligence of many kinds of software have made it quicker and easier to detect suspicious transactions.
However, this is not without its own set of challenges to overcome. Though transaction monitoring systems can expedite the often tedious process of screening a bank’s daily operations, some may be less thorough than others. Additionally, maintenance processes might also be too lax or too stringent, which may directly affect how well the systems work. All these pose a new set of problems that can be solved by ensuring that the software is correctly tuned at all times. With that in mind, here are some of the key challenges that come with using new banking software and what can be done to fix them:
Too Many False Positives
While rule-based systems are more sensitive and effective than machine-learning artificial intelligence, an excessive number of false positives come up during transaction monitoring. What banks can do about this is to have staff members dedicated to constantly testing and troubleshooting their software to correct these issues. The good thing about rule-based AI is that the rules can be tweaked and changed to improve and tune the monitoring system.
Whenever online banking forms are incorrectly filled out or left blank, the system may not be able to read the inputted data correctly. This can result in certain transactions being overlooked or incorrectly flagged by the transaction monitoring system. While the algorithm may need tweaking, measures to reduce human error are also needed. Clear instructions and restrictions that ensure your customers properly fill out your forms can help correct this issue.
Incorrect Structuring Pattern Detection
Criminals tend to deposit their stolen money in small amounts to avoid suspicion. They may also quickly withdraw their money the moment they get their withdrawal confirmation. These structuring and velocity patterns can be evidence for suspicious activity. Unfortunately, other banking customers who mean no harm can also have their transactions incorrectly flagged for doing the same thing.
One way to correct this is to ensure that the software is tuned to consider customer profiles in addition to transaction patterns. Taking the time to tweak the rules to allow the system to recognize patterns and match it to stored customer information can result in more effective transaction filtering and detection of suspicious activity.
Fragmented Loops of Feedback
There are times when the event scoring procedures in monitoring systems do not occur in real-time. In the past, historical data was typically replicated manually to ensure that there were no lapses in the information that may distort any final outcomes. This process was often time-consuming and usually took a few days to complete, causing the data to become outdated by the time it could be used for tuning. That being said, it is essential to ensure that feedback loops present complete and proper data in automated systems.
Banks usually keep a list of individuals and organizations that are exempt from their transaction monitoring AML systems for one reason or another. This is another measure that can help minimize the level of false positives given their trusted customers’ proven track records. But while it is good to honor loyal banking clients who have a history of honest and ethical transactions, it is still completely possible for their behavior to change in the future.
Without someone to monitor and regularly update these lists, it’s easy for individuals or organizations to take advantage of the fact that they’re not being monitored for illegal activity. An automatic system makes it easier to keep track of this list of exclusions and update it every so often. The list can then be fact-checked by a specialist to correct any errors.
As a way to reduce the number of false positives, many banks suppress a lot of alerts in an effort to tune their transaction monitoring software. However, suppressions need to be done carefully to avoid illegitimate transactions from going undetected by the system. Without this tuning in place, the software can make things more difficult for the bank when it comes to automated transaction monitoring.
It is important to employ a multi-disciplinary approach to ensure that banking software expedites processes for customers, detects illegal activity, and remains compliant. For additional insight and assistance, banks can hire an expert in financial crime and compliance management to help improve their automated systems. With continuous maintenance, any anti-money laundering software can be successful in helping banks provide a secure banking experience to their customers.
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