Reduce False Positives in AML: Best Practices and Examples in 2024

Are you wondering how to reduce false positives in AML? Well, hundreds of thousands of bank transactions are scanned every day, looking for any signs of fraud or suspicious activity that might be linked to money laundering. Bank systems are usually flagged if a large sum of money is transferred between two accounts. If this transfer is much larger than usual, it looks suspicious, and the system assumes that it might be a red flag or a warning sign that needs a more in-depth investigation.

However, in many cases, it appears that the transaction is legit. For example, say the transaction turned out to be a payment for a rare antique painting sold at an auction. The buyer transferred the funds to the seller's account as agreed, but the bank's systems mistakenly flagged it as suspicious.

Yes, banks and other financial institutions need to detect real fraud to protect their customers and report any activity associated with money laundering to comply with AML laws and regulations. But if the number of misinterpreted activities surpasses a certain threshold, then the bank has an issue; this issue is called false positives. 

What are False Positives in AML?

In simple terms, a false positive occurs when an innocent legal financial activity is flagged as suspicious. This innocent activity raises an alert and then undergoes a manual review to decide whether it is an actual red flag or a false positive. 

The Impact of High False Positive Rates

It is essential for financial institutions to monitor transactions and comply with AML regulations, but once the false positive rate gets high, the burden on investigators increases. In fact, false positives are one of the most challenging issues associated with AML transaction monitoring because when a transaction triggers a certain rule, then the investigation team will be notified, and the more notifications and alerts they get, the more manual work they will be doing, and this is in fact impractical.

Rule-Based Systems and Transaction Monitoring

When we’re talking about transaction monitoring, we’re talking about monitoring each and every single transaction activity, which includes deposits, purchases, merchant credits, payments, fund transfers, and so on. So, Imagine how many alerts the investigation team will get if the bank uses a rule-based system. A rule-based system usually starts the process by scanning transactions for money laundering red flags, and when a transaction triggers a rule, an alert is created and sent to the bank’s investigation team for review. Now, if the investigation finds the activity suspicious, the team files an SAR, AKA Suspicious Activity Report.

However, if the investigation finds that the activity is legitimate, then this incorrect alert forces the compliance team to spend excessive time dealing with false positives, which in turn reduces the overall efficiency of the team!

What Causes False Positives?

Now that we’ve explained that banks should work towards reducing their high rate of false positives, we should explore the original reasons that play a significant role in increasing the percentage of false positive incidents. 

1. A rule-based approach has significant drawbacks, and its most notable issue is the high rate of false positives. A rule-based system is inflexible and cannot adapt to the complex patterns of money laundering.
   
   
2. If the data used for monitoring lacks clarity or contains errors, this data will lead to misinterpretations and, thus, false positives.
   
   
3. If the bank is using an AML system that relies on outdated rules and/or models, this may cause irrelevant and inaccurate alerts.
   
   
4. Traditional AML systems can get overwhelmed by large volumes of transactions or diverse types of activities.
   
   
5. With the lack of context or understanding of the business operations and customer behavior patterns, AML systems will most probably misinterpret legitimate activities as suspicious. 

False Positives vs. False Negatives

It is not always a false positive, there are also false negatives. False negatives are the incidents that the investigation team is actually looking for. Now the system sometimes flags a transaction activity as suspicious when in fact it is not! But when the system fails to detect actual suspicious or fraudulent activity and mistakenly classifies them as legitimate, then we're facing another issue which is false negatives.

Assume that the bank is using overly lax rules, outdated detection systems, or a system with insufficient monitoring capabilities, then a money launderer will most probably successfully hide illegal transactions as normal ones. False negatives are more dangerous than false positives because they allow illegal activities to go unnoticed and potentially lead to legal and financial repercussions for institutions.

False Positives vs. False Negatives: Key Differences

• Detection Errors: False positives are incorrect alerts on legitimate actions, while false negatives are missed alerts on fraudulent actions.

• Consequences: False positives result in wasted resources and customer inconvenience, whereas false negatives pose a significant risk by allowing fraud to go undetected.

In other words, false positives and false negatives are two types of errors in monitoring systems. False positives incorrectly flag legitimate actions as suspicious, causing inefficiencies and customer issues. False negatives miss actual threats, posing serious risks.

AML systems must strike a balance between avoiding too many false positives (to reduce unnecessary work and customer friction) and minimizing false negatives (to ensure all fraudulent activities are caught).

It's important to reduce false positive alerts to avoid unnecessary investigations and costs. Consistent efforts to reduce false positive instances improve operational workflow.

8 Consequences of High False-Positive Rates

Let’s start by explaining what it means by a high positive rate so that we can understand its consequences better.

Many rule-based transaction monitoring systems flag all transactions over $10,000 for anti-money laundering (AML) review, and to combat 'structuring' tactics, a new rule requires alerts for transactions just under this threshold, such as those over $9,800.

Now, if fifty transactions are flagged and forty-five turn out to be false positives, the false-positive rate is 90% (45 out of 50 transactions flagged as suspicious). However, with twenty false positives, the rate drops to 40%.

A high false-positive rate reflects the effectiveness of AML transaction monitoring systems and techniques. Unfortunately, organizations grappling with high false-positive rates face several consequences:

1. Costly Manual Reviews: High false-positive rates lead to significant administrative and financial burdens, primarily through the AML case management process.
   
   Each flagged transaction prompts a Suspicious Activity Report (SAR), necessitating investigators to handle these reports. When multiple SARs are filed for one customer, law enforcement gets involved, prolonging the process and tying up administrative resources for extended periods.
   
   
2. Time Constraints and Compliance Issues: Manual review processes for AML alerts are time-consuming. A high false-positive rate increases the volume of these reviews, jeopardizing compliance with reporting deadlines, which are typically within 30 days of detecting suspicious activity.
   
   
3. Focus Shift: The institution may need to shift its strategic focus and resources to address the high false-positive rates.

4. Impact on Customer Experience: Constantly flagging customers for AML investigations can irreparably damage the customer relationship. This can also increase customer friction as legitimate customer transactions may be delayed or blocked, which will cause inconvenience and frustration.
   
   
5. Legal Costs: Potential legal fees and settlements if customers pursue action due to repeated inconveniences.
   
   
6. Loss of Business: Customer dissatisfaction due to transaction delays and blocked accounts can lead to loss of business and reduced profitability.
   
   
7. Regulatory Scrutiny: High false-positive rates can draw regulatory attention, as they may indicate inefficiencies in the AML program.
   
   
8. Missed Alerts: Resources spent on false positives may lead to missed opportunities to detect actual suspicious activities, which, in turn, can increase the risk of non-compliance.

Example of False Positives in Rule-Based AML System

Customer: Sarah Smith, a small business owner who frequently receives payments from various suppliers and makes regular international transactions for importing goods.

Bank: XYZ Bank is a mid-sized financial institution with a rule-based transaction monitoring system.

Sarah Smith runs a successful small business importing specialty goods. Her company regularly receives payments from suppliers abroad and makes frequent international transfers to settle invoices. XYZ Bank, where Sarah holds her business accounts, has a stringent rule-based system for monitoring transactions.

• A Large Wire Transfer Raises Red Flags: One day, Sarah initiates a large wire transfer to pay for a shipment of goods from a new supplier in Asia. This transaction triggers XYZ Bank's automated monitoring system due to its size and international nature. The system flags the transaction as potentially suspicious, citing criteria such as large amounts and international transfer patterns consistent with money laundering.

• XYZ Bank's Compliance Review Process: Upon receiving the alert, compliance officers at XYZ Bank manually review Sarah's account or transaction history. They find that similar transactions have occurred regularly in the past without issue. However, due to the rule-based system's inflexibility, the transaction is escalated for further manual review as per regulatory requirements.

• Resolution of the False Positive: After a thorough investigation involving multiple compliance checks and consultations with Sarah, it became evident that the transaction was legitimate. Sarah provided invoices, supplier contracts, and shipment details to verify the business purpose of the transfer. However, it turned out to be a false positive, and Sarah’s transaction was a legitimate business transaction misidentified by the bank's rule-based system.

How to Reduce False Positives in AML: 6 Best Practices

Understanding how to reduce false positives can greatly enhance the accuracy of your AML processes. Training staff on how to reduce false positives ensures everyone is aligned with best practices. In this section, we introduce 6 best practices to reduce false positives in AML.

1. Implement a Risk-Based Approach

First and foremost, you will need to make sure that your team's resources are concentrated on transactions that are more likely to be suspicious. This can be achieved by adopting a risk-based approach rather than a rule-based one. In a risk-based approach, your focus will be on high-risk profiles and activities.

Banks are constantly seeking new methods to reduce false positives in their monitoring systems. Successful efforts to reduce false positives lead to greater efficiency and customer trust.

2. Use a Dynamic Risk Scoring System

When you get new data, the risk score should be updated accordingly. Also, you will need to clean and update your data regularly. Why is that? Because it will maintain accuracy and reduce misidentifications.

3. Refine Detection Rules

In compliance, monitoring is not a one-time process but rather a regular, continuous one! You need to set up a schedule for periodic reviews of detection rules so that they remain efficient against the evolving tactics and methods of money laundering. But remember to always test these new rules in a sandbox environment to evaluate their impact on false positive rates.

Effective false positive reduction strategies are essential for maintaining compliance and efficiency. Regularly updating these strategies helps in continuous false positive reduction.

4. Optimize Alert thresholds

Not only rules but also alert thresholds need review. Make sure to regularly review and adjust alert thresholds based on performance data. This will actually help you reduce false positives in AML because it maintains a balance between specificity and sensitivity.

5. Continuous Monitoring and Improvement

Regular system audits will enable you to identify inefficiencies in the AML system, and the findings will direct you to make the necessary adjustments. At this point, you might want to get feedback from compliance officers, who can provide suggestions for system enhancements and areas for improvement, which will, in turn, reduce false positives in AML.

6. Use Advanced AML Software

An advanced AML system deploys machine learning algorithms that can detect subtle patterns and anomalies that indicate money laundering and hence improve detection while reducing false positives in AML.

Machine-learning AML software also learns from new data and past false positive cases to refine their accuracy over time.

Advanced AML software also supports dynamic rule adaptation, real-time monitoring, and scenario customization. The system automates routine boring tasks, freeing up your team to focus on more complex instances and investigations.

Implementing advanced algorithms is key to effective AML false positive reduction. By focusing on AML false positive reduction, banks can ensure more accurate and reliable transaction monitoring.

Learning how to reduce false positives in machine learning can significantly improve the performance of predictive models. Advanced training on how to reduce false positives in machine learning helps in refining these models for better accuracy.

Example of False Positives in an AI-Powered AML System

Bank: XYZ Bank is a larger financial institution that has implemented advanced AI-driven transaction monitoring solutions.

Sarah Smith, still operating her business, decides to switch her banking services to XYZ Bank, which boasts an AI-powered transaction monitoring system known for its accuracy in identifying suspicious activities while minimizing false positives.

• Real-Time Analysis of a Large Wire Transfer: When Sarah initiates a similar large international wire transfer to pay for a new shipment of goods, XYZ Bank's AI system quickly analyzes the transaction in real time. The AI system considers various factors beyond transaction size and destination, including Sarah's historical transaction patterns, supplier relationships, and industry norms.
  
  
• AI System's Risk Assessment and Smooth Operations: Instead of immediately flagging the transaction, the AI system assigns it a risk score based on these factors. The score is below the threshold that would trigger a manual review, as the system recognizes the transaction as consistent with Sarah's usual business activities.

• Seamless Banking Experience for Sarah: Sarah receives a notification confirming the transfer without any delays or additional scrutiny. She continues to operate smoothly without disruptions, and XYZ Bank's compliance team focuses its efforts on higher-risk activities that genuinely require attention.

Comparison Between Example #1 & Example #2

The first bank (Rule-Based System) faces challenges with high false positives, requiring extensive manual reviews and potentially disrupting legitimate business operations like Sarah's.

The second bank (AI Automation) minimizes false positives by utilizing AI to accurately assess transaction risks based on comprehensive data analysis. This allows for smoother operations and enhanced customer satisfaction.

While both banks aim for regulatory compliance and fraud prevention, the adoption of AI in transaction monitoring significantly reduces false positives in AML. It decreases operational disruptions, which provides a more efficient and customer-friendly banking experience.

Reduce Your Bank’s False-Positive Rate with FOCAL

High false positive rates in AML transaction screening and monitoring can have extensive consequences. Banks often spend considerable resources investigating these alerts, only to discover that most do not indicate illegal activity. This not only escalates operational costs but also diverts resources from detecting and investigating real threats.

FOCAL AML Transaction Screening & Monitoring can assist banks in refining the scope of data deemed relevant for the AML alert review process. By customizing monitoring scenarios to their specific risk profiles, institutions can more effectively identify genuine risks and reduce false positives associated with generic rules.

Conclusion

In conclusion, false positives happen when real transactions get flagged as suspicious. This can cause big problems and waste time and money. High false-positive rates not only waste effort and increase costs but also upset customers.

By reducing false positives, financial institutions can save time and resources while improving customer satisfaction. Effective techniques for reducing false positives also enhance the overall security framework.

To reduce false positives in AML, it's important to tell them apart from false negatives and use best practices. Using advanced AI systems like FOCAL, which are more accurate and flexible, can help cut down on these false alerts. With smart algorithms and constant checking, banks can reduce false positives in AML, ensuring they follow rules better and work more efficiently, while focusing on real suspicious activities.