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How to Leverage Machine Learning for AML Compliance

14:56 18 January in Blog

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. AML regulations and procedures help organizations identify, monitor, and report suspicious transactions and provide an additional layer of protection against financial crime.

Money laundering is a serious threat in the financial services industry and in the online gaming and casino industry. In fact, online casinos as an industry carries the biggest risk of money laundering. Global consultancy firm, Deloitte, estimates that the amount of money laundered globally in one year is in the range of US$800 billion to US $2 trillion.[1]

With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. It helps to protect organizations from financial losses, reputational damage, and regulatory penalties.

How Machine Learning Helps Detect and Prevent AML

ML algorithms identify patterns in customer behavior which could point to money laundering activities, monitor customer behavior for any sudden changes in spending patterns, any suspicious account activities, and other potential indicators of fraud.

There are primarily two underlying techniques that can be leveraged for AML initiatives- Exploratory Data Analysis and Predictive analytics.

Exploratory Data Analysis (EDA)

EDA is used to analyze data and summarize their main properties and characteristics using visual techniques. Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data.
In this case, once a customer’s documents are scanned and uploaded, the necessary data is extracted from the key documents and then converted to machine-readable form. An automated process is then developed for swift verification. Thus, speeding up the entire process with minimal error.

EDA might be used to identify any unusual patterns or trends in the customer’s financial records, or to identify any connection or relationship with other entities that may be of concern. EDA can also be used to detect anomalies and inconsistencies in the data that may suggest that the client is providing fraudulent or misleading information.

The underlying technology used to convert the scanned image to machine readable format is called ‘Optical Character Recognition’ (OCR) or text recognition analysis. OCR is widely used to digitize all kinds of physical documentation.

Predictive Analytics

It is a subset of business analytics that uses statistical techniques (algorithms) to find patterns in historical data points and predict future outcomes with high accuracy. For predictive analytics to deliver high accuracy, a lot depends on the combination of domain knowledge and technical expertise. With the exponential growth of large datasets, predictive analytics is being leveraged by enterprises across industries. Predictive Analytics can help businesses in reducing risk (eg. Credit Risk Analysis) maximizing opportunities (predicting Customer LifeTime Value) and improving operational efficiencies (eg. optimizing inventory) by identifying trends and gathering insights from large volumes of data.

Different Use-cases of ML for AML initiatives

Automating ‘Know Your Customer’ (KYC) processes:

KYC process helps to identify customers, verify their identity and assess their risk of being involved in money laundering by understanding the nature of customers’ activities and validating their source of funds as legitimate. The process involves verifying customer data against various sources manually. Under AI supervision, the algorithm automatically flags suspicious users.

Automated transaction monitoring and risk assessment of customers:

Automated transaction monitoring systems use machine learning algorithms to detect suspicious activity in customer transactions and alert organizations to any potential money laundering activities. The algorithms can detect anomalies in the transactional data and helps to identify high-risk customers and transactions that may be linked to money laundering activities.

Predictive modeling for flagging suspicious activity

Predictive analytics can be used to analyze past customer behavior and transactions to identify patterns that may indicate potential money laundering activity. By leveraging predictive analytics, organizations can proactively identify and prevent money laundering before it occurs. Money dumping or poker chip dumping is a frequent form of money laundering witnessed in online casinos and poker sites that depend on predictive analytics to detect any suspicious activity.

Steps to building a highly accurate predictive model for AML

It is now easier than ever to deploy ML solutions thanks to the recent chain of innovations introduced by major industry players like AWS and Microsoft. There are a number of open-source ML platforms like KNIME that can also be leveraged to detect and predict suspicious behavior.

Building a predictive model is a continuous process and commitment. Each step is extremely important and demands a lot of attention from data scientists. These include-

Data Cleansing and Refinement:

A key step in the predictive modeling process involves assessing the quality and usefulness of existing data in terms of missing values, outliers and other anomalies. This not only helps you avoid reporting invalid results down the line but also forms a crucial step in building a solid foundation for your predictions.

Feature Engineering:

For predictive analytics to deliver high accuracy, a lot depends on domain knowledge and expertise. Feature engineering is the process of using domain knowledge of the data to create attributes that make machine learning algorithms work. The process involves selecting and creating attributes that are relevant for the specific problem. This may include combining variables, creating new variables based on existing ones, and scaling the data.

Model Selection:

A good model selection is one of the most critical steps in predictive analytics. This could include supervised learning models such as random forests, decision trees, and support vector machines, or unsupervised learning models such as clustering algorithms. It is important to review how well each possible model fits with your data before making predictive model selection choices.

Model Training:

The selected model is trained on a dataset and subsequently validated and tested before being deployed. The process includes using cross-validation to optimize the model’s performance, and parameter tuning to adjust the model’s hyperparameters. How well a model performs during training will determine how well it will perform when it is implemented in an application for end users. Hence, optimizing the model is necessary to increase the accuracy and efficiency of the model.

Model Deployment:

Deploy the model in production and monitor its performance. This could include deploying the model to an API or web service, and setting up an alert system to monitor the model’s performance. This is not the final step.

Refine the model:

Machine learning applications require meticulous attention to optimize an algorithm. Refine the models based on feedback from users and performance data to ensure that the models are accurate and reliable. This is a continuous cycle as customer behavior is known to keep evolving at a fast pace and it is necessary to keep identifying inefficiencies in the algorithm.

To conclude:

To combat money laundering and avoid being scrutinized by regulators, organizations must

  • Establish clear policies and procedures to flag suspicious customer activity. This includes customer due diligence, continous risk assessments, defining responsibility of the employees and senior managemen among others
  • Monitor customer activity across multiple channels to ensure that all transactions are legitimate including tracking customer activity on online banking, mobile banking, credit card, and other payment methods.
  • Encourage customers to report any suspicious activity. Make sure you have processes in place for customers to quickly and easily report any suspicious activity they may have noticed.
  • Provide suspicious activity reports to relevant authorities to ensure that money laundering activities are reported and investigated.
  • Stay up to date with regulations: Stay up to date with the latest regulations and security measures to ensure that your customers’ data is protected.
  • And keep refining and revisiting the algorithms and models for optimal efficiency.


[1] Anti-Money Laundering Preparedness Survey Report 2020: