With the right AI model and mix of internal and third-party data, you can adopt a better AML system for detection and prevention.

Dutch bank ABN Amro recently agreed to pay a $574 million settlement after an investigation found that the bank failed to identify suspicious clients, didn’t terminate their accounts and didn’t report money laundering schemes to the authorities. This news story illustrates the drastic consequences of failing to meet anti-money laundering (AML) requirements. It’s a serious issue since banks are faced with an increase in money laundering. Besides, criminals are using more complex schemes that span several countries and different banks.

It’s time to build a more effective AML process to stay one step ahead of these schemes. You can upgrade your fraud prevention solution by using data more effectively and leveraging new tech like AI.

The growth of money laundering

Money laundering accounts for anywhere between $800 billion and $2 trillion. It represents 2-5% of the world’s GDP. However, the true extent is difficult to assess since criminals use advanced techniques like layering to make money hard to track.

And while scrutiny has been increasing, criminals have also been using more complex tactics. For instance, a growing number of money launderers are using virtual currencies to remain anonymous. Some criminals are turning to trade-based laundering to avoid traditional financial institutions too.

Wire transaction schemes also have remained a constant in the money laundering landscape. In fact, recent data shows an increase in wire transactions. As a result, banks are struggling to monitor high transaction volumes.

In the UK, 74% of executives filed more suspicious activity reports in 2020 compared to 2019. Another source reports that fines linked to anti-money laundering failures were more frequent in the first half of 2020. Indeed, there is a recent surge in money laundering that seems to be tied to COVID-19.

Top challenges banks face with their anti-money laundering process

Too many banks use outdated AML methods. These systems are no match for the number and scope of modern money laundering tactics.

Here are some of the top anti-money laundering challenges banks face:

  • Siloed data and a lack of collaboration between internal departments result in limited visibility. This allows criminals to use advanced schemes. Money can go through several banks, and money launderers can keep using the same methods since banks don’t share information.
  • Banks often rely on manual processes. Manual reviews are time-consuming.
  • A study found that as much as 95% of alerts are false positives. Many fraud prevention teams are overwhelmed and more likely to make mistakes.
  • Legacy systems and other old technology can be obstacles to collecting valuable data.
  • New criminal tactics can be hard to recognize.
  • Keeping up with regulations and avoiding hefty sanctions are additional challenges.

Using advanced AI and collaboration for a strong AML compliance process

You can build a better anti-money laundering process with automation. Automation allows you to keep up with high volumes of transactions. Plus, it reduces errors linked to manual reviews.

With the right AI model and mix of internal and third-party data, you can adopt a better AML system for detection and prevention.


You need to identify the right internal data sources and adopt a solution for centralizing data.

You should also upgrade your know-your-customer AML, or KYC AML, program. Looking at more data points can give you a better idea of who users are. It also results in a better experience for them.

From identifying customers to monitoring transactions, your KYC program can be a strong line of defense against money laundering.

AML collaboration

Internal data isn’t enough. Criminals are using advanced schemes that span several countries and target different banks. You need more data sources and deeper insights.

In other words, you need to work with third-party data. With anonymized data from other banks, you can prevent criminals from using the same method twice.

With a strong AML collaboration system in place, you can deter criminals and prevent money laundering instead of merely reacting.

AI and machine learning

AI and machine learning models are powerful tools for analyzing data. You can extract valuable insights from several data sources with AI.

The latest models can sift through large amounts of data to keep up with high transaction volumes. Plus, you’ll get almost instant insights and alerts.

AI can also classify alerts based on how serious the threat is. Your fraud prevention team can use this information to focus on high-level threats.

How Fraud.net can help with anti-money laundering

Fraud.net can help you face the growing threat of money laundering with quality data from third parties and AI models that automate this process:

  • AML identity screening and monitoring and transaction monitoring. Identify anything suspicious and adopt a better KYC AML process.
  • Application AI. This tool assesses risks for each application your process, looks for anomalies and compares information with consortium data to flag fraudulent identities.
  • Collective Intelligence Network. Our network improves AML collaboration between banks to spot complex schemes and known criminals.

Are you ready to improve your anti-money laundering process? Contact our experts today for a demo.