Why you need an automated AML transaction monitoring solution

We’re living in the digital age, meaning we’re all too familiar with how the online sphere has dominated the world of financial transactions, particularly in recent years. Naturally, a consequence of this is that cybercrime and online money laundering have risen exponentially. Automating your AML transaction monitoring process, rather than doing it manually, will give you superior results with increased consistency – while you save time and money. Here’s a bit more on why you need to make the switch to an automated AML transaction monitoring solution ASAP.

Your customers and brand deserve it

Sure, theoretically you can opt for the manual route, but a manual transaction monitoring process is time consuming and error prone which can lead to a negative customer experience. More importantly, if your competition opts for automation before you do, then you fall behind and risk compromising your brand and the success you’ve earned so far.

Regulators will look at the leading names using automation when devising new regulations, leaving you to play catch-up. The days of having this information examined manually are long gone – the digital age waits for no one. Automation is inevitable, so you might as well be proactive, seize the opportunity, and get ahead of the curve. You’ll be amongst the leaders, rather than reacting to what others have done before you.

Increased accuracy and consistency

A team of people can never be as consistent as an automated system. Compliance officers cannot function at peak efficiency for long, with factors like fatigue and human error coming into play. Work rates will inevitably slow down over time, and mistakes will happen. Aside from all this, manual monitoring does not have the scalability that its automated counterpart offers – which is significant for any company attempting to keep up with global regulations.

The bottom line is this – purely manual methods are a thing of the past. We’ve seen automation on the rise across virtually all industries, at least in some capacity, and the financial sector is no different. Every financial organisation needs to facilitate maximum efficiency while being able to scale and adapt accordingly. With that in mind, there remains no doubt that automated AML transaction monitoring is the way forward.

ComplyRadar – it pays to comply

ComplyRadar helps address AML transaction monitoring requirements by automatically identifying suspicious behaviour in real-time or on a scheduled basis, while minimising false positives. It monitors transactions related to individuals, accounts, and entities to detect suspicious activity quickly and effectively, through a fully audited process to inspect and act on flagged transactions. Contact us for more information on how ComplyRadar can empower you to stay on top of the ever-changing regulatory requirements and make the right risk decisions, faster.

What is AML transaction monitoring?

Even if you’re not directly involved in the Banking & Finance or Gambling & iGaming sectors, there’s a pretty good chance that you’ve at least heard – or read – about Anti-Money Laundering (AML). It refers to a set of laws, regulations, and procedures intended to prevent criminals from disguising illegally obtained funds as legitimate income. Even though AML laws cover a relatively limited range of transactions and criminal behaviours, their implications are far-reaching and nearly impossible to detect without the right AML transaction monitoring software.

AML transaction monitoring software helps businesses operating in the Banking & Finance or Gambling & iGaming sectors automatically monitor their customers’ financial transactions so that suspicious activity can be identified in real-time. A proper automated system will achieve the following:

  • Minimise false positives by applying a risk-based approach and adapting scenarios to customer and/or transaction risk levels.
  • Spot patterns and outliers by monitoring behaviours alongside historical transaction data and other contextual profile data.
  • Check each customer transaction against their individual profile to identify any transaction that does not meet their historical behavioural patterns.
  • Compute running values and statistics to define complex scenarios that need to consider past activity.

How AML transaction monitoring software works

AML transaction monitoring software looks at everything from deposits and withdrawals to international wire transfers, currency exchanges, credit extensions, or any kind of payments in or out of accounts. By identifying patterns over time, the system learns to predict your customer’s actions, detect any unusual (potentially suspicious) behaviour, and send out immediate alerts to your compliance team for further investigation. 

Companies big and small use this technology to fight against all kinds of fraud and money laundering including structuring, double invoicing, and round-tripping – but that’s not all it does. This sophisticated software also plays a central role in sniffing out terrorism financing and any customers who might be trying to avoid international sanctions. The main benefits of AML transaction monitoring software are as follows
 
  • Comply with global regulations
  • Protect your reputation
  • Instil a positive customer experience
  • Minimise risk exposure
  • Avoid potential fines

ComplyRadar – it pays to comply

ComplyRadar utilises a full risk-based approach to eliminate disruption to genuine customers, detect potential criminal behaviour, and demonstrate full ongoing compliance. It sends you notifications on the transactions that matter and enables you to automatically apply a full-pattern analysis to instantly see suspicious transactions in real time. You can then manage flagged transactions through a comprehensive review process leading to the filing of a SAR when required. Contact us for more information on how ComplyRadar can help you address the AML transaction monitoring requirements by automatically identifying suspicious behaviour in real-time.

Using AI and machine learning to fight money laundering

Data scientists have done a great deal for us in the way of making our financial lives easier and, without a doubt, more secure than ever before. These advancements aren’t always used for good, however. While technology has brought the financial world forward in leaps and bounds, it’s worth remembering that this same technology is used for far more nefarious reasons: cybercrime in its many forms.

The Cost Of Money Laundering

Fighting money laundering is a massive, costly mission that needs to be perpetually in motion. For context, anti-money laundering (AML) measures cost European banks roughly €18 billion each year, with their US counterparts shelling out approximately €22 billion annually.

Banks all over the globe need to have their fingers firmly on the pulse of the fintech ecosystem, and that means paying close attention to their transaction monitoring standards. Failure to do so could leave banks with a hefty fine. The last ten years have seen a whopping 90% of Europe’s banks slapped with fines for failing to take the required AML measures – that’s €23 billion on fines alone.

Changes in the tech landscape

Staying at the forefront of tech is crucial for any organisation hoping to remain relevant in a highly competitive industry. AI and machine learning are developing at breakneck speed, with the biggest cloud providers in the industry making them a clear priority over the last few years.

This tech has the potential to completely revolutionise the front and back-end operations of financial bodies across the globe, boosting risk management efforts, maximising efficiency, and reinforcing the effectiveness of financial crime investigations across the board. Embracing this new tech can also minimise costs by helping financial institutions meet regulations more efficiently, freeing up human resources that can be assigned to other vital areas of the business in the process.

Types of AI and machine learning

There are two types of AI and machine learning, each with its own set of pros and cons – supervised and unsupervised. Supervised learning involves using a model trained using data that has already been categorized to raise a red flag on to any transactions that seem suspicious.  In unsupervised learning, what happens is that raw, uncategorised data is introduced to the system, making it start from scratch. By interacting with that data, the system starts to identify patterns indicative of money laundering activities while also creating new ways to sort and analyse data.

The bottom line

As intelligent as this tech may be, it’s only as good as the data you feed it, so investing in talent should remain a top priority. You can’t expect any model employing AI to work without any sort of human input or testing – not yet, at least. Take transaction monitoring, for example. Each transaction needs to be evaluated against a set of risk-based rules. Even the most advanced monitoring systems to date leave banks with a substantial number of false positives, and that’s when the reviewer comes in to cast a human eye over the results before moving forward. In order for any financial institution to get the best out of its human and tech resources, the two need to work hand in hand. Employees need to feel empowered to work with machines and AI, and tech needs a human element in order to continue moving the industry forward.

ComplyRadar to the rescue

ComplyRadar monitors transactions relating to individuals, accounts, and entities to detect suspicious activity – quickly and effectively. Its machine learning transaction filter provides an additional probabilistic layer on top of the rules engine. This not only drastically reduces false positives but also provides additional data output which is not being captured by rules. For example, the machine learning algorithm can identify that a particular new combination of data is resulting in suspicious transactions. Contact us for more information on how ComplyRadar can automatically identify and react to suspicious behaviour in real-time or retrospectively while minimising unnecessary alerts.