AML False Positives: How To Identify and Reduce Them

• July 28, 2024
• Rachna Pandya

Your AML system flags so many transactions as suspicious, only to find out later that they were perfectly legitimate. This phenomenon, known as false positives, can be a real headache.

What Are AML False Positives

AML false positives occur when a transaction or an account is incorrectly flagged as suspicious. This means the system thinks something is wrong when everything is actually fine.

False positives in AML can account for up to 95% of anti-money laundering (AML) alerts in some systems! These false alerts can waste valuable time and resources, making it harder to focus on real threats.

Examples of False Positives in AML

🚩A regular customer’s large purchase sets off an alert.

🚩Routine foreign transactions flagged just for being international.

🚩Multiple small deposits mistakenly seen as structuring attempts.

🚩Known clients flagged because of minor personal info errors.

🚩Transactions outside business hours causing unnecessary alerts.

Do you often notice these common false flags in your AML system? Let’s first find out what causes false AML positive alerts before learning how to identify and reduce them.

What Causes False Positives in AML

False positives in AML (anti-money laundering) systems happen when regular transactions are mistakenly flagged as suspicious. Here are some common reasons why this occurs:

• Data quality issues: When the information in the system is wrong or outdated, it can lead to mistakes, flagging normal transactions as suspicious.

• Broad rule-based systems: Many AML systems use set rules that might be too broad or sensitive. This means they can catch normal activities by mistake.

• Complex algorithms set inaccurately: Sometimes, the advanced algorithms used can misinterpret the data, especially if they aren’t set up correctly.

• Lack of contextual information: Without enough background information, the system might flag transactions that look unusual at first but are actually normal when you see the full picture.

How To Identify AML False Positives In 6 Steps

Identifying false positives is key to making your AML process work better and using your resources wisely.

1. Regularly Review Flagged Transactions

• Check the transactions your system marks as suspicious on a regular basis.

• Look for patterns or similarities among these flagged transactions to figure out why they are being marked.

2. Examine Examples to Refine Rules

• Use specific instances of false positives to adjust your system’s rules and settings.

• For example, if your system flags large cash deposits from local businesses, tweak the rules so these types of transactions are treated correctly.

3. Analyze Customer Historical Data for Anomalies

• Compare flagged transactions with the customer’s past activity to spot any unusual patterns.

• Understanding how a customer normally behaves helps you tell the difference between genuine issues and normal transactions.

4. Manually Review Alerts to Verify Authenticity

• Look at alerts yourself to check if they are genuine or just false alarms.

• This step helps confirm whether the flagged transactions are actually suspicious.

5. Conduct Pattern Analysis to Understand the Causes

• Find and study patterns in the false positives to understand why they keep happening.

• Identifying common factors can help you adjust your system to reduce these false alerts.

6. Use Machine Learning to Improve Detection Accuracy

• Apply machine learning tools that can learn from past data to better understand what is normal for each customer.

• These tools help cut down on false positives by getting better at recognizing the context of transactions.

Following these steps will help you identify false positives and make your AML process more accurate and effective.

How To Reduce AML False Positives In 6 Steps

Here are some straightforward steps to cut down on AML false positives:

1. Adjust Thresholds for Alerts

Fine-tune your system’s sensitivity settings. Make sure the thresholds are appropriate for different types of accounts to avoid unnecessary alerts.

2. Improve Data Quality for Accuracy

Regularly update and clean your data. Accurate, up-to-date information helps your system make better decisions.

3. Refine Rules and Algorithms to Better Fit Your Context

Customize the rules your system uses. Continuously tweak and improve algorithms based on what you learn from past alerts.

4. Integrate Contextual Information for Better Accuracy

Add more data points, like customer profiles and transaction histories, to give your system more context. This helps distinguish between normal and suspicious activities.

5. Leverage Advanced Analytics and Machine Learning

Use machine learning to teach your system what normal behavior looks like for each customer. These technologies can help filter out false positives more effectively.

6. Regularly Update Data to Stay Current

Keep your customer information up-to-date. This makes it easier to spot unusual transactions and reduces false alarms.

AML False Positives as a Major Compliance Challenge

False positives AML systems aren’t just annoying—they can cause big problems for your company. Here’s why they’re a serious issue:

• Regulatory scrutiny: If your system flags too many false positives, regulators might start watching you more closely. This can lead to more audits and potential fines.

• Operational costs: It takes a lot of time and effort to check each alert. When most of them turn out to be false alarms, it’s a waste of resources that could be used to catch real threats.

• Customer friction: If legitimate transactions get flagged often, it frustrates your customers. They might get annoyed and lose trust in your service.

• Resource allocation: By reducing false positives, your team can focus on actual suspicious activities, making your AML efforts more effective.

Final Word on AML False Positives

Dealing with AML false positives can feel like playing a never-ending game of whack-a-mole. False positives waste time and resources, and they can frustrate your team and customers. By understanding the causes—like data quality issues, overly broad rules, and lack of context—you can start to tackle the problem. Keep your data clean, adjust your system rules, and use machine learning to make your alerts more accurate.

Remember, the goal isn’t just to have fewer alerts, but to have better ones. Quality over quantity is key. By reducing AML false positives, you can focus on real threats and improve your overall AML compliance process. Keep your system sharp and up-to-date, and your team equipped with the latest tools and knowledge. You’ve got this!

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Top FAQs On What Are AML False Positives

What is the false positive rate in AML?

The false positive rate in AML refers to the percentage of alerts flagged as suspicious that turn out to be legitimate activities upon further investigation. High false positive rates can lead to inefficiencies and increased operational costs.

What are false positives in KYC?

False positives in KYC occur when the system mistakenly flags a legitimate customer as suspicious or risky. This can happen due to matching errors or overly strict compliance criteria.

What is a false positive name screening?

False positive name screening happens when an individual’s name is incorrectly flagged as a match against a watchlist or sanctions list. This often results from common names or similarities to listed entities.

What is an example of a false positive?

An example of a false positive is a bank transaction flagged for exceeding a threshold amount, even though it’s a routine payment from a trusted client. This triggers unnecessary alerts and reviews.

What does a false positive indicate?

A false positive indicates that a system has flagged a legitimate transaction or customer as suspicious, necessitating a review to confirm the error. It reflects the need for better-tuned algorithms and data accuracy.

What is a false negative in AML?

A false negative in AML is when a genuinely suspicious activity goes undetected by the system. This failure can lead to potential regulatory violations and missed opportunities to prevent financial crimes.

What are false positives and true positives?

False positives are legitimate activities incorrectly flagged as suspicious, while true positives are actual suspicious activities accurately identified by the system. Balancing these is crucial for effective AML operations.