Common Shortcomings of Traditional Transaction Monitoring Methods

Most people tend to think of corruption and financial crimes as victimless wrongdoings that occur at no real cost. After all, no one is physically harmed by white-collar felonies such as identity theft or fraud. It’s all too easy to believe that no “real” money is lost, either. Those who have been victimized by these types of crimes typically only need to report them to the appropriate agencies to straighten things out.

Unfortunately, financial crimes such as money laundering and terrorist financing do affect millions of people. These misdeeds fund heinous illegal activities such as child labor, human trafficking, and prostitution in some of the world’s poorest and most vulnerable areas.

In fact, it’s a trillion-dollar industry. According to estimates from the United Nations Office on Drugs and Crime, about 2 to 5 percent of the global GDP is laundered globally through legitimate banking systems each year. Though that number may sound small at first, it’s an astronomical amount of money, ranging from USD 800 billion to 2 trillion.

Transaction monitoring is an integral component of the anti-money laundering and financial crime compliance systems that financial institutions use to protect themselves from becoming unwitting participants in these types of crimes. Unfortunately, criminals are only growing more daring and technologically-savvy, rendering traditional transaction monitoring methods inefficient and obsolete. Below are a few of these old systems’ shortcomings:

Poor Data Integrity

One of the biggest challenges that financial institutions face when monitoring customer transactions is making sure that the data that they are using is complete and accurate. Often, technology architecture grows by acquisition instead of through organic means, with new data systems tacked on top of old ones. This means that some organizations may be using multiple platforms and processes to satisfy their compliance obligations.

The situation, therefore, creates a disparity in the information used to perform transaction monitoring, making it difficult to ensure that all relevant data is obtained and presented in a usable format. Additionally, poor data integrity also brings down the quality of the alerts received by the financial institution, causing investigations teams to waste time chasing unproductive leads. These issues can easily be addressed by adopting a modern, unified transaction monitoring system that can leverage all structured and unstructured data as well as integrate with external data sources. 

Lacks Holistic Oversight

Effective transaction monitoring is so challenging because it necessitates a complete and holistic view of a customer’s account. Without transaction monitoring AML software, the best approach would be to have an employee manually stop and review every transaction made by a customer before it is allowed to push through. That employee would require access to historical and current information about the customer and all their interactions in addition to every incoming and outgoing transaction to and from their account. Such an endeavor would not only be ridiculously inefficient, but would also be exorbitantly costly for the organization. 

Furthermore, while traditional rules-based transaction monitoring can detect suspicious activity based on certain behaviors, the surveillance process involved typically does not factor in behaviors that occurred before and after each specific instance. For truly impactful transaction monitoring, financial institutions should employ a state-of-the-art solution that incorporates multijurisdictional transaction filtering. Ideally, it should be able to successfully detect and process different forms of data embedded within financial transactions across various business lines. 

High Levels of ‘False Positives’

Traditional rules-based transaction monitoring systems identify criminal activity by automatically screening customer actions against a pre-determined set of regulations. Should an action break a rule, the system generates an alert and flags the transaction for further investigation by a human compliance officer.

One of the major issues with this approach is an overly simplistic application of the rules. Uniformly applying these rules throughout a bank or financial institution’s entire customer base, as well as every single transaction, may not be ideal. According to a report from Microsoft Azure, traditional transaction monitoring systems tend to generate huge numbers of false-positive alerts. This problem is so pervasive that most banks experience a false positive rate of anywhere between 95 to a whopping 99 percent.

Given that each alert must be attended to by a human investigator, the results can be downright catastrophic. The efficiency of an institution’s anti-money laundering efforts is also drastically reduced as the situation creates a significant backlog of cases pending investigation. It also reduces a compliance officer’s capacity to spend time reviewing alerts for transactions that may be truly suspicious. Having to deal with a large number of false positives may also lead to investigator fatigue, which can contribute to human error. All in all, the formulaic way that traditional transaction monitoring systems work can lead to real illicit activities going uninvestigated and criminals getting away scot-free.

Fortunately, most modern transaction monitoring software solutions are prepared to tackle these issues with event scoring applications that utilize machine learning and algorithmic models to reduce false positives. 

Eschewing traditional transaction monitoring systems and methods for more modern approaches can deliver a bevy of benefits to your business. Truly, upgrading your systems can improve data integrity, enable your organization to get to know your customers better, and empower your compliance officers to do their jobs more efficiently.