Data scientists have contributed a lot in making financial lives easier and, without a doubt, more secure than ever before. However, these advancements are not always used for good.

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.

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 decade has 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.

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 new technology can also minimise costs by helping financial institutions meet regulations more efficiently, which frees up human resources that can be assigned to other vital areas of the business.

There are two types of AI and machine learning, each with its own set of pros and cons – supervised and unsupervised. Supervised learning involves a model trained using data that has already been categorised to raise a red flag on any transaction 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.

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.

For instance, take transaction monitoring – 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 making a final decision.

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 to continue moving the industry forward.

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 standard rules engine. This not only drastically reduces false positives, but also provides additional data output which is not being captured by simple rules.

For more information visit www.comply-radar.com or e-mail info@computimesoftware.com.