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Why transaction monitoring needs AI

By 3 minute read

As the world continues on a trajectory towards near-total digitization, the emphasis on fighting money laundering, terrorist financing, and other financial crimes using cutting-edge technology is growing.

Many of the previously analogue formats of fighting financial crime are now in need of technological solutions to keep up with the digital world, transaction monitoring needs AI to handle the rapidly evolving landscape of payments and remittances. 

This is largely down to digitization itself, which has enabled the rapid growth of online banking and other transactional activities—investing, money transfers, currency exchange, purchases; it’s now easier than ever to access all of these things and more. Naturally, this is piling significant pressure on financial institutions that are expected to thoroughly monitor a rapidly growing number of non-cash transactions and quickly detect suspicious activity. 

White paper: The use of AI in AML Transaction Monitoring

Analogue transaction monitoring is finished

Unfortunately, it’s the case that many banks and other financial institutions continue to use outdated, archaic tools and operate with immature ecosystems that rely on manual human input. Think Microsoft Excel pivot tables and manual reviews of transactions. 

These human-led processes that use legacy technologies simply cannot keep up and operate at the scale needed in a digital economy where the majority of transactions take place online. This is because transaction monitoring and AML in general is very intense, data-heavy work, and the workload is multiplying each day. It’s simply the case that human operators cannot keep up and machines are better at handling it. 

By way of example, the e-commerce industry alone is expected to hit $5.5 trillion globally this year and continue growing at a rapid rate thereon. And that’s just an example of one industry. With the pace of digital transformation that we’re currently seeing across all industries, especially those involving finance, firms that refuse to accept digitization and move with the times will eventually find themselves facing serious issues and potential trouble with regulators further down the line when they cannot keep up with their compliance obligations.

Interestingly, the UK’s Financial Conduct Authority was recently hiring for a ‘Director of Innovation’ role that will be tasked with driving “engagement with technological innovation within the financial services industry”—if this isn’t a clear sign that financial institutions should be abandoning their legacy processes and beginning to adopt technology within their workflows, we don’t know what is. The writing’s on the wall and you need to pay attention.

AI-powered transaction monitoring

The only viable solution for firms nowadays is to apply artificial intelligence (AI) and machine learning (ML) to their tech-led (not human-led) transaction monitoring. This will help them to build a robust decision support system by leveraging ML-powered predictive analytics which uses self-learning to continuously evolve with new data points and customer analysis. 

Although it is possible for firms to deploy their own AI/ML ecosystems and apply transaction monitoring technology after the fact, many opt to use transaction monitoring solutions that are already AI/ML-powered so that they can keep their focus on deploying fast and automatic real-time transaction monitoring. Using third-party tooling that has AI at its core comes with the added benefit that very little maintenance is required when compared to the amount of developer-resource that would be required if you were to rely on your own AI and ML infrastructure. For most firms, it just makes sense to choose the former.  

Why transaction monitoring needs AI

AI and ML have been game changers for many sectors, and finance is no exception. One reason transaction monitoring needs AI is that costs are rising as non-cash transaction volumes grow year on year, which has consequently led to large increases in the number of suspicious transactions that need to be looked at by compliance teams. 

Together, AI and ML provide an opportunity for firms to reduce the operational costs associated with transaction monitoring by automating the process on an unprecedented scale. Over time, AI-backed transaction monitoring systems can begin to understand transactions and build pattern recognition to quickly identify AML typologies, monitor high-risk jurisdictions, identify suspicious movement of funds, screen against sanctioned individuals, and highlight spikes in the value or volume of transactions. This makes the transaction monitoring process more accurate, efficient, and cost-effective as a result, well beyond what could be achieved by a human-led process. More importantly, however, it keeps you in line with your compliance obligations and reduces the risk of becoming the subject of punitive action by regulators.  

AI is the answer

Given the current state of play with regard to the digital transformation, it can easily be argued that an AI-backed approach to transaction monitoring is the only viable solution due to the unrelenting increase in global non-cash transaction volume that it is enabling. This coupled with the inherent fallibility of humans makes for quite the compelling case for adopting an AI- and tech-led approach to transaction monitoring. 

By replacing legacy human-led systems with an AI/ML transaction monitoring platform, banks and other financial institutions can substantially reduce operational costs, tackle unbalanced data, improve alert prediction over time, increase reporting accuracy, and ensure compliance with evolving regulations. 

Transaction monitoring needs AI tools like Sentinels make AML transaction monitoring a simple, painless process. Offering 360-degree automated coverage, it is the key to successful AML. If you’re interested in how a tool like this could help protect your bottom line, request a free demo of our transaction monitoring solution today.

White paper: The use of AI in AML Transaction Monitoring