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Transaction Monitoring

What is transaction monitoring in AML?

Transaction monitoring is the process of monitoring customer transactions as part of anti-money laundering (AML) regulations because financial institutions must observe client behavior to abide by laws and regulations in the countries in which they operate.

Transaction monitoring involves assessing customer information, the value of historic and recent deposits, withdrawals and transfers, and customer interactions.

Because so many transactions occur each day, financial institutions use software – the transaction monitoring system (TMS) – which typically uses information gleaned from know your customer (KYC) processes that the financial institution already has in place to ascertain the client’s risk to the institution.

The TMS automatically analyses client data for anomalies, highlighting potential risks that require further manual investigation.

How does an AML transaction monitoring system work?

Traditionally a TMS works using a rules-based system. Should a transaction break a rule, it may be flagged for review.

Manually monitoring every transaction is impossible but it does mean that some questionable movements of money will occur. Financial institutions must therefore decide on their risk level to ensure that they prevent money laundering as best they can while still abiding by regulations and offering their customers the best user experience.

A customer risk score is assigned by looking at everything from their nationality and residency to their profession, and many other categories.

Alongside looking at the value of a transaction, monitoring also includes looking at whether customers are PEPs (see PEP – Politically Exposed Persons) or on any sanctions lists.

Why is AML transaction monitoring important?

Financial regulators around the world have made transaction monitoring a regulator requirement. As such, it is now a vital part of meeting AML and counter-terrorist financing (CTF) requirements.

As well as this, the information gleaned from monitoring transactions can be used for filing suspicious activity reports (SARs) and for meeting other legal obligations.

By spotting and stopping questionable transactions with financial crime prevention software, an institution can prevent millions of dollars from being laundered each year. This has a knock-on effect on illicit activities which might include not just terrorism but also the illegal arms trade, drug smuggling, or sex trafficking.

As might be expected, a low-risk customer won’t need as much monitoring as a high-risk customer, and financial institutions can adjust their monitoring accordingly.

Having a good TMS in place gives trust to consumers and regulators. It shows that the financial institution, whether a bank or an insurance company, is doing all it can to meet regulations and prevent criminal activity from occurring.

Is automated or manual transaction monitoring in AML most effective?

Automated transaction monitoring in AML is far more effective than a manual approach in every area. It is far faster and cheaper to use anti-financial crime software to monitor transactions, such is the volume of data that financial institutions are dealing with on a day-to-day basis. Alongside this, humans are prone to error in a way that machines aren’t.

Although automated transaction monitoring is better for financial institutions to use, manual monitoring is still required. Because suspicious transactions are discovered using rules, a transaction monitoring system may flag something that is suspicious when it isn’t. E.g. A person receiving or spending a lot of money prior to a big life event (getting married, buying a house, etc.) A manual approach from a financial crime investigator is therefore needed to assess whether events have been flagged correctly.

These false positives are the main issue with using automated software. Rules have to be accurate enough to capture suspicious transactions but not so broad that too many false positives occur. This ensures that financial crime investigators aren’t simply spending their time checking obviously non-suspicious transactions when their time could be better spent on genuinely suspicious events.

Thankfully, rules can be customizable depending on the risk level that a financial institution is comfortable with, as well as to follow unique rules for different client types (e.g. somebody working in television who may be paid large sums of money infrequently).

What should you look for in an AML transaction monitoring solution?

An AML transaction monitoring solution should be customizable and scalable for a financial institution to use effectively. This is because it is important for institutions to be able to follow changing regulations as and when they occur.

Alongside this, it is important for anti-financial crime teams to be able to create an audit trail of activity. This is for two reasons – to aid in their own investigations when one team member takes over from another and also to send on to authorities if suspicious transactions are occurring.

Increasingly, it is also becoming important for transaction monitoring solution to use AI. If it is, investigators can be a lot more thorough in their searches while also hugely increasing their productivity when it comes to creating a narrative summary for colleagues or writing a suspicious activity report (SAR).

What is a suspicious activity report (SAR)?

A suspicious activity report (SAR) is a key part of the transaction monitoring process. For more information, please see Suspicious Activity Report (SAR).

What is transaction laundering?

Transaction laundering is slightly different to money laundering. Transaction laundering is where criminals create legitimate transactions as a means to launder illicit goods.

Imagine a criminal selling weapons or drugs. They can’t set up an online merchant that advertises the selling of these goods, but they could set up a website that ‘sells’ niche items that appear to be legitimate.

Once the order goes through, the transactions appear to be above board. However, the purchase has actually been placed for the illicit goods sold by the criminal. Because it appears to be a legitimate website to a financial institution, it therefore satisfies the KYC checks they have in place.

The subterfuge works on both sides; the transaction won’t raise alarms for the purchaser or the seller, whose receipt will list the fake website.

Detecting transaction laundering can be difficult but can be made possible through entity resolution as part of customer due diligence (CDD). This allows financial institutions to understand who the beneficial owner of a company is, while they can also analyze the merchant website and connected entities, to better understand the nature of the business.

How does transaction monitoring differ from transaction screening?

Although both processes occur as part of robust anti-money laundering procedures, transaction monitoring identifies suspicious patterns in transactions over time whereas transaction screening refers to analyzing individual transactions for suspicious activity.

What problems can occur in transaction monitoring?

There are many ways that financial institutions can run into trouble with their AML transaction monitoring due to infrequent checks. Here are just three:

A TMS system may flag too many transactions that do not warrant manual review. These are known as false positives. This slows down investigation teams attempting to catch genuine criminals, increasing operational costs as a result.

Fewer than 1% of flagged transactions ordinarily result in escalation. As such, an enterprise must regularly review its TMS to address the number of false positives while also making its system robust enough to catch questionable transactions.

AML transaction monitoring can suffer from oversimplification. By wanting to cut down on false positives, a financial institution may group clients and activities into one scenario when this doesn’t solve the issue. In fact, it will eventually lead to more false positives over time when clients behave differently to the scenario in which they’ve been placed.

By using greater granularity when segmenting customers, the monitoring is more likely to be accurate and allow for iterations over time.

An institution might have too many rules in place when monitoring transactions with their anti-money laundering software. This can lead to duplication of cases as well as difficulties in managing all of the new scenarios created. This is where a holistic approach to client data can help. By centralizing everything, it is much easier to monitor the transactions and understand what is going on. One such solution is the Sensa Investigation Hub.

SymphonyAI’s AML transaction monitoring software

SymphonyAI’s NetReveal Transaction Monitoring software allows users to discover hidden risks, streamline AML investigations, and dramatically reduce false positives with an AI-led SaaS solution. Save valuable time and resources while reducing risk exposure with advanced machine learning algorithms ensuring that your organization is performing to its full potential and minimizing the risk of regulatory fines or reputational damage.

Enjoy smooth monitoring operations, automated manual tasks, and a holistic view of risk alongside faster investigations and optimized alert management. Efficient, effective and designed to stay ahead of evolving regulatory requirements, enact ongoing state-of-the art compliance and eliminate the need for costly, disruptive system changes with accurate, seamless transaction monitoring.

Learn more about NetReveal Transaction Monitoring.

SymhponyAI’s SensaAI for AML

Alongside SymphonyAI’s NetReveal Transaction Monitoring software, the company also offers SensaAI for AML. This augmentation strengthens existing systems and eliminates the need for a complete overhaul by arming investigators with powerful AI capabilities. Enhance rule-based detection methods and identify the latest criminal behaviors targeting your business and its networks, all while significantly reducing false positives.

Learn more about SensaAI for AML.

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