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Transaction monitoring in 2022 – how payment providers need to meet AML demands

Transaction monitoring in 2022

Money laundering is, quite literally, big business. The UN estimates anywhere from $800 billion to $2 trillion is laundered each year worldwide. That equates to between 2% and 5% of global gross domestic product, roughly the same as the entire agriculture industry.  

Regulators are putting increasing pressure on financial institutions (FIs) to clamp down on money laundering, and payment providers haven’t escaped their attention. Several new regulations targeting the payments industry, such as the EU’s second Payment Services Directive (PSD2), have been published over the last few years, while the risk of illegal activity has risen as the pandemic boosted the volume of online transactions.   

One of the key responsibilities for payment providers under anti-money laundering (AML) regulations is to monitor transactions. So in today’s post, we’re going to explore what that involves and how HAWK:AI can help streamline the process.   

What is transaction monitoring?  

There are two stages to the AML process. The first is during onboarding when an FI must verify a new customer’s identity, referred to as ‘know your customer’ (KYC). This layer of protection is designed to make sure that all participants in the financial system are who they say they are.  

The second is once the customer starts transacting and continues throughout the lifetime of the relationship. The remit then switches to monitoring those transactions for any suspicious behaviour indicating a bad actor might be using the financial system for illicit purposes. FIs must also repeat KYC checks regularly.    

Money muling is an example of the kind of transaction FIs have to prevent. Money mules receive funds into their bank account from a third party and then transfer it to someone else in return for a fee. While not directly involved in the crime, they’re considered accomplices as they help to launder the proceeds.  

Breaching AML rules exposes FIs to heavy penalties and severe reputational damage. According to risk consultancy Kroll, global regulators issued fines totalling $2.2 billion for money laundering offences in 2020, five times higher than the previous year. Australia’s Westpac bank paid the biggest fine of $940 million for allowing customers to send money to the Philippines where it was allegedly used to facilitate child exploitation.  

Building blocks  

An effective transaction monitoring system consists of three ‘building blocks’.  

The first is data. It comes in two forms- live data from processing payments and static data from customer relationship management (CRM) systems. CRM data is described as static because it’s gathered during the KYC stage (such as personal details and the customer’s risk profile) and rarely changes.  

The second is a detection engine. Data feeds into the detection engine which applies rules to transactions to determine whether they pose a risk. One of those rules could involve screening for an unusually high frequency of transactions with a single counterparty. Monitoring can be performed manually, although most FIs automate the process to cut costs, save time and remove friction from the customer journey. Technologies like artificial intelligence (AI) are helping to accelerate it even further.    

The third building block is the user interface. When a transaction comes up as suspicious, the interface sends an alert prompting the compliance team to investigate it. At this point, human input may be required to review the transaction and the customer’s track record. This intervention determines whether the system has flagged an illegal transaction or a false positive (see below), in which case it can proceed.  

What are the challenges?  

The biggest challenge for payment providers is finding a transaction monitoring solution that fits into their infrastructure quickly and without creating friction. That means partnering with a vendor who understands the IT infrastructure used by payment providers and the unique issues they face. The vendor must also apply agile methodologies such as constant collaboration and incremental development. A further consideration is how the vendor deploys the solution. Most leverage cloud technology because it simplifies the integration process and it’s scalable, so users don’t have to invest in additional hardware to meet growing demand.  

Another challenge for payment providers is the high frequency of false positive alerts, where an AML solution mistakenly flags a transaction as suspicious. The rigid and rules-based nature of these solutions, many built on legacy infrastructure, means that 95% of alerts turn out to be false positive. Manual counterchecking adds friction to the customer journey and increases costs for FIs.   

What does the future hold?  

Payment providers are under increasing scrutiny when it comes to monitoring transactions. The number of regulations is growing- in the last couple of years, providers have had to adapt to PSD2 and the fifth and sixth Anti-Money Laundering Directives (AMLD5 and AMLD6), not to mention the dawn of open banking. A lot of providers have had to upgrade their infrastructure to meet the new requirements.   

A recent trend, that seems set to stay, is the change in consumer behaviour triggered by the pandemic. To put this trend in context, Amazon’s sales rose by 27% in the second quarter of 2021 compared to a year earlier. Lockdown forced people to become more comfortable with online transactions, but as the volume has risen, so has the risk of illegal activity. Payment providers have no choice but to scale up their monitoring efforts accordingly.  

How can HAWK:AI help?  

In short, by further automating the process of monitoring transactions. That starts at the integration stage. HAWK:AI’s flexible API allows providers to seamlessly integrate with our platform without adding complexity to the payment infrastructure. Once connected, our platform uses a combination of rule-based models and AI to monitor transactions (in real time, where necessary), an approach which reduces false positives and identifies suspicious behaviour more accurately. 

We train our AI algorithm using transaction data gathered from each client, either on an ongoing basis through our case manager or historical data from the last 12 months. We then feed it into the algorithm which learns how to replicate the client’s human decision-making process and automates it moving forward.  

We also provide a user interface with drag and drop functionality so it can be easily configured to respond to changes and a list of scenarios reflecting the current state of regulations, which users can monitor against.  

To find out how HAWK:AI can streamline your transaction monitoring, or to arrange a demo, get in touch today.  

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