How Feedzai Embeds Next-Generation Anti-Money Laundering Solution Optimization

Rules-based legacy systems simply aren’t able to address the sophistication of today’s financial crime tactics. Banks need to optimize their anti-money laundering (AML) alerts to effectively detect and intercept suspicious activity. 

Here’s how Feedzai addresses the challenge using patented AI technology to optimize AML alerts — without requiring banks to immediately retire existing rules-based systems entirely. 

4 AML Challenges for Compliance Officers

Before looking at how next-generation anti-money laundering solutions can help, let’s first look at the common pain points caused by rules-based legacy systems.

Many Banks’ AML Systems Were Built in the Pre-Digital Age

The financial services industry has experienced a digitalization boom in the past few years. However, many existing AML systems use a rules-only approach to transaction monitoring. Without next-generation anti-money laundering methods in place, banks’ legacy systems will be unable to meet the demands of the digital banking workflow. The result is a large amount of false positive alerts. This leaves compliance teams scrambling to address non-issues instead of focussing on the real threats. 

Compliance Officers Feel Burned Out

It’s estimated that about 95% of a bank’s system-generated alerts are false positives. Banks’ compliance officers must address this struggle to remain focused and avoid being disheartened. A recent survey found that nearly half (49%) of compliance officers feel overwhelmed at least weekly. If compliance officers can’t trust the quality of AML alerts, they risk spending valuable time on unproductive suspicious activity reports (SARs). In other words, compliance professionals often chase false positives instead of focusing on high-value alerts and identifying financial crimes. No wonder so many compliance officers feel burned out on the job.

Despite Heavy AML Investments, Banks Still Face Fines 

Banks worldwide spend billions on financial crime compliance each year. Yet despite these heavy investments, global regulators fined banks roughly $5 billion for anti-money laundering failures. That’s a 50% increase in fines issued a year earlier. In other words, after investing large sums of money to fix a problem, the problem persists. 

Banks clearly need a more effective alternative if they want to stay ahead of financial crime trends. This means adopting next-generation anti-money laundering solutions for next-generation challenges while causing minimal disruption to existing processes. That’s how Feedzai’s AML solution optimizes alerts for more effective compliance.

Barriers to Embedding Machine Learning in AML

Machine learning is critical to enhancing AML reporting. However, there has been some reluctance in the past to implement it. There are several reasons for this. First, regulators have been busy clarifying the guidelines for appropriately using artificial intelligence (AI) and machine learning. For example, an EU-based challenger bank was recently fined for using AI in its AML workflows by its central bank but successfully overturned the decision in the courts.

Second, while some banks understand machine learning’s potential, they are concerned that the investment required to shift to machine learning will mean too much disruption for their operations. Taken together, banks have been reluctant to invest in machine learning technology.

How Feedzai Delivers Next-Generation Anti-Money Laundering Solutions

These are significant obstacles to machine learning implementation for AML operations. But Feedzai’s AML solution optimizes alerts to help reduce compliance officers’ workloads. Here’s how.

Compliance Teams Appreciate Optimized AML Alerts

Next-generation anti-money laundering capabilities start with more effective AML alerts. Feedzai’s AML optimization capabilities prioritize the most critical alerts, creating a domino effect that addresses many other obstacles outlined here. Compliance officers will be able to focus on higher-priority alerts. These alerts require the greatest attention because they are more likely to result in a SAR filing. When the teams are at capacity, they will know exactly what to address first without feeling they’ve missed potentially critical alerts. In other words, AML optimization can reduce work fatigue.

Keep Existing Rules-based Systems

Many banks put off implementing AI and machine learning investments. This shift can be especially challenging for banks that still rely on rules-only legacy banking systems. Fortunately, banks that cannot immediately shift away from their rules-based system can still benefit from using machine learning for AML. Instead, banks can keep their legacy systems and add machine learning capabilities on top of them. It also helps evaluate existing governance and risk-based approaches with an experienced vendor. 

Embed AML Optimization into Existing Solutions

Feedzai’s AML optimization capability can enhance existing systems by adding an intelligent machine learning layer. With this approach, banks don’t have to wait until a contractual agreement with another vendor expires. They can start seeing the benefits of next-generation anti-money laundering solutions immediately. 

A rules-based approach to AML is insufficient in the digital age. Banks need to embrace next-generation anti-money laundering solutions to meet today’s financial crime challenges. They also need to optimize their AML alerts to reduce false positives, employee burnout, and avoid fines. Machine learning is the smart way to supplement existing rules-based systems and deliver on regulatory priorities.