How AI is Transforming the Fight Against Money Laundering

How AI is Transforming the Fight Against Money Laundering

Money laundering is a heinous crime and one that is becoming increasingly more difficult to get away with due to the power of AI. Thanks to the ever-growing capabilities of AI, law enforcement agencies can now detect patterns in financial data that would otherwise be invisible to the naked eye. This allows them to identify suspicious behaviour and take timely and effective action.

What is Money Laundering?

Put simply, money laundering is the process by which criminals disguise the source of their income. They do this by making it appear as though it was obtained from a legitimate source or for a legitimate purpose, such as an investment. In reality, however, the funds may be stolen, earned through illegal activities, or simply taken from innocent victims.

Why is Money Laundering a Problem?

Money laundering has several serious consequences, both for the individual perpetrators and society as a whole. For one thing, it allows criminals to hide their assets and evade prosecution. It also creates an environment in which criminal activity can thrive and, by extension, support the spread of more crime. When criminals feel like they can get away with their crimes without repercussions, it gives them less cause to worry about what they’re doing. This is why money laundering has such a big impact on public safety.

How is AI Changing the Fight Against Money Laundering?

AI has increased the effectiveness of the fight against money laundering in several ways. First, it can help investigators sift through large amounts of data to identify patterns that would otherwise be hidden. By running all the data they have on a given suspect through an AI system, they can uncover behaviour that is far too complex for even the most adept humans to detect. This also allows investigators to identify new kinds of criminal behaviour as soon as it emerges. Criminal behaviour is constantly evolving, so law enforcement agencies must be able to keep up with criminals’ latest tactics and techniques. With the help of AI, they can do just that – keeping a step ahead at all times.

In addition, AI can reduce the risk of human error during investigations. As we all know, people are fallible and mistakes happen; however, when it comes to money laundering investigations, one error could cost authorities a criminal and leave his or her victims forever unsatisfied with how justice was served. This is why many law enforcement agencies choose to adopt an approach that is more automated and less reliant on human analysis. AI offers a better approach when it comes to hunting down criminals in large sets of data.

Finally, AI can identify connections between cases that may otherwise go undetected. In many instances, money laundering investigations span across multiple jurisdictions because the crimes committed are often not limited to one area or country. This poses a challenge to investigators because, while they have access to information that would be valuable in another case, it’s not always clear which data is relevant. However, the power of AI means that connections become visible even when they don’t seem immediately obvious. Therefore, law enforcement agencies can get more accurate results with fewer resources. 

Machine Learning in Anti-Money Laundering

The application of machine learning in the financial sector for anti-money laundering (AML) is not a new topic. It has been around for over a decade, but only recently has it begun to be applied at a large scale within the industry. The goal of this paper is to provide an overview of how machine learning is being applied to combat money laundering, including its limitations. Most of the research in this paper is focused on supervised machine learning algorithms because it is still the most popular technique used by industry today. An outline of these techniques will be provided, along with code examples using Python and R showing how to implement them for anti-money laundering purposes.

The use of machine learning for AML started in the early 2000s when big data started to become a big thing. At that time, it was mainly used for fraud detection in credit card companies. A few years later, machine learning was being used by banks to detect unusual behaviour in customer accounts. Nowadays, it is used for a wide range of purposes by banks under different names. For example, anti-money laundering teams often refer to their algorithms as ‘risk engines’.

AML Machine Learning Techniques

The most popular techniques in use are logistic regression, decision trees and random forest. These are also the most well-known machine learning algorithms in the finance industry. In addition, artificial neural networks and support vector machines are also used as well as ensemble methods such as bagging and boosting. Logistic regression is a type of supervised learning algorithm that attempts to predict an outcome with a set of input variables. The goal is to give a numerical value that represents the probability that a certain event will happen. In the context of AML, this could be the likelihood that a customer is engaging in money laundering activity.

There are two main types of logistic regression: binary and multiclass. The former is used when there are only two outcomes (e.g., fraud or no fraud), while the latter is used when there are more than two outcomes (e.g., fraud or no fraud, but also money laundering). Logistic regression is a very flexible method that gives good results even if the input variables are not predictive. However, it has been proven to be less effective in cases where the input variables are too numerous and correlated with one another. 

Decision Trees 

Decision trees create a model that starts at the top and branches out as it progresses down to the bottom of the tree. The way it does this is by splitting each node (intersection) based on some criteria (variable). Once reaching the base, we can make our prediction, for example, is the customer engaged in money laundering? The benefit of a decision tree is that it is easy to understand and does not require any fancy math. In addition, it is also very resistant to overfitting. 

Random Forest 

Random forest is another type of supervised learning algorithm that works by creating multiple decision trees in parallel. This is done by creating a random set of variables at each node and then selecting the best one to split the node on. This is repeated until all the nodes have been split. The benefit of using a random forest is that it is more resistant to overfitting than a decision tree. In addition, it also performs better when the input data is high dimensional (i.e., has a lot of variables)

Artificial Neural Networks 

Artificial neural networks are a type of unsupervised learning algorithm that is similar to the brain. It consists of several interconnected neurons that can learn to recognise patterns. The advantage of using an artificial neural network is that it can handle nonlinear data better than other methods. This is because the neurons can be re-wired to handle more complex relations, instead of splitting the data in two as a decision tree would. However, they are also highly susceptible to overfitting

Support Vector Machines

Support vector machines (SVM) is another unsupervised learning algorithm that creates hyperplanes between different clusters of input variables. By doing this, it attempts to create a finite set of categories that can then be used to predict the outcome. In the context of AML, the support vector machine would attempt to create a finite number of categories such as high risk and low risk based on pre-determined variables (Breiman, 1996). If this is successful, we can assign each customer to either one category or the other.


Bagging is a type of ensemble method that is used to reduce bias and improve accuracy by generating multiple models. It does this by creating bootstrap samples with replacements from the original dataset. This allows it to create new datasets for training purposes, which can then be combined using averaging or voting.

Ensemble Methods 

Ensemble methods are a set of supervised learning algorithms that work by combining the predictions of multiple models. This is done to improve the accuracy of the predictions and reduce bias. There are some different ensemble methods available, but the most common are boosting and bagging. Boosting works by creating a model and then adjusting it by giving more weight to those cases where the prediction was incorrect. Bagging works by creating many bootstrap samples with replacements from the original dataset. This creates multiple models, which can then be averaged or voted upon to make a final prediction.

The advantage of using an ensemble method is that it can improve the accuracy of the predictions. This is because it increases the diversity of the models used, which allows them to capture more of the variability in the data. In addition, it also helps to reduce the bias in the predictions.

AI can help with the fight against money laundering, but it’s not perfect. There are many aspects of this problem that will need to be addressed for us to make progress on solving our complex financial crimes. That said, there is no doubt that artificial intelligence has the potential to transform how we fight corruption and crime across industries—this includes finance. 

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