Machine Learning and its Role in Fraud Detection and Anti-Money Laundering Compliance

With the growth of generative AI tools, businesses are using new technologies to detect fraud and money laundering. One of them is machine learning.

Machine learning tools can learn complex transaction patterns, enabling businesses to proactively monitor customer behavior, therefore more accurately identifying and preventing risks.

In this article, we’ll observe what machine learning is, how it’s used in different industries, and how it can be best used to prevent fraud and money laundering.

Machine learning: what is it and how is it used in anti-money laundering?

Machine learning is a field of artificial intelligence (AI) that enables computers to learn, predict, and make decisions without being explicitly programmed.

Machine learning algorithms are designed to analyze and process vast amounts of data, identify patterns, and make informed predictions or decisions based on this information.

In anti-money laundering analytics and compliance, machine learning can be for the following: 

• Identity verification at onboarding. Machine learning algorithms can assist in verifying client identities by analyzing various data points, including personal information, biometrics, and behavioral patterns.
• Document verification. Machine learning models can be trained to analyze documents, such as passports, driver’s licenses, and IDs. These systems can extract necessary information from documents, compare it to reference data, and detect potential inconsistencies. They can also flag forged or altered documents.
• Transaction monitoring. Machine learning systems can process large amounts of transaction data. 
• Fraud and money laundering detection. By analyzing historical fraud patterns and continuously monitoring transactions in real-time, machine learning models can identify and flag potentially fraudulent activities.
• Ongoing monitoring. Machine learning algorithms can also be used to continuously monitor customer behavior patterns based on historical data. These algorithms can learn what constitutes normal behavior for each customer, such as typical transaction amounts, frequency, geographic locations, and other factors.

Machine learning in fraud detection

The role of machine learning in the detection of fraud and other criminal activity is growing. Here is how it can be used in the field.

Detection of deepfakes and other spoofed images, videos, and audios

Today, machine learning plays one of the most important roles in detecting deepfake fraud, which is growing day after day. As deepfake techniques become more sophisticated, so do the detection methods:

1. Detection of artifacts not present in authentic media. Deepfakes often contain certain visual or audio artifacts that are not present in authentic media. Machine learning algorithms can be trained to detect these artifacts by analyzing specific features of digital content, such as inconsistencies in facial expressions, unnatural eye movements, or distortions in sound waves.
2. Detection of deepfake generation techniques. Machine learning algorithms can identify traces left by specific deepfake generation techniques. These models can learn to recognize the unique characteristics introduced during the deepfake generation process.

Behavioral fraud detection

Machine learning can be used to analyze customer behavior patterns in order to detect fraud. This analysis processes a huge set of data, such as usual login times, device types, typical transaction types and amounts, and even styles of keyboard/mouse use. Here machine learning algorithms can be applied in the following ways:

1. Profile-based analysis and anomaly detection. Machine learning algorithms can create profiles based on historical data and customer behavior, and remember patterns of normal behavior for individuals and groups. Trained on historical data, these models can flag suspicious transactions, user activities, and other behavioral deviations. This way, multiple fraud types can be detected, including account takeovers or identity theft.
2. Ongoing learning. Machine learning models can continuously learn and adapt from new data, allowing them to stay up-to-date with evolving fraud techniques. 

Document forgery detection

Machine learning can help with document forgery detection in the following ways:

1. Understanding document features. Machine learning models can extract relevant features from documents that are indicative of forgery, including texture, font, signatures, stamps, watermarks, etc.
2. Verification of signatures. Machine learning techniques can be applied to verify signatures, comparing a signature on a document with a reference signature. These algorithms can analyze stroke patterns, pressure, and thus recognize unique features of genuine signatures—differentiating them from forged signatures. 
3. Detection of forgery in digital documents. Machine learning models can analyze metadata, digital signatures, or compression artifacts and detect traces of manipulation. These algorithms can also check the textual content of digital documents to identify inconsistencies, plagiarism, or content alterations which indicate forgery.

Machine learning in transaction monitoring

Transaction fraud is on the rise, with total losses expected to exceed $48 billion in 2023. A reliable Transaction Monitoring tool is essential for any business today, especially in the financial industry, and here machine learning may also be a great help.

Machine learning systems can process large amounts of transaction data and detect behavioral anomalies and suspicious activities in financial transactions, customer profiles, and historical patterns. 

These models can learn from labeled data (e.g., known fraudulent transactions) to identify patterns that indicate money laundering or other fraudulent activities. They can also learn from unlabelled data (e.g. by clustering) and use it to detect unusual patterns.

Businesses following AML regulations often want to use certain rules, according to which transactions are flagged as suspicious. If AI is used to enforce these rules, such models arrive at conclusions without explaining how they were reached. Therefore, today the challenge for machine learning in AML compliance is to create reliable AI AML software that provides understandable rules explaining the model’s conclusions.

Machine learning in banks and other segments

Machine learning can be used in banking and financial services as follows:

• Fraud detection as part of AML compliance
• Risk assessment and credit scoring
• Trading and investment strategies
• Chatbots and virtual assistants for customer support.

The future of machine learning technology

According to Statista, the market for artificial intelligence (AI) is expected to grow substantially in the coming years. Currently valued at $100 billion, the market is expected to grow twentyfold by 2030, up to nearly two trillion USD.

Today the AI market covers a vast amount of industries and professional fields, including financial services, supply chains, marketing, product making, research, and analysis. More fields are expected to adopt artificial intelligence within their business structures.

As democratization of modern technologies continues, digital fraud and deepfakes become more sophisticated and easier to create. This cannot stay unnoticed. Regulators worldwide are expected to start paying closer attention to AI-related technologies and their application in business.

In light of the above, today companies are recommended to:

• Closely monitor new fraud trends
• Monitor AI-related AML regulations
• Invest in technological development.

AML AI software

When evaluating an anti-money laundering artificial intelligence software, it’s important to consider the needs of your organization. In general, the following features make for a reliable AML AI solution:

1. Security standards. A reliable software should adhere to robust security standards to protect sensitive information and ensure data privacy. It should have measures in place for data encryption, access controls, authentication, and secure data storage.
2. Rule-based alerts. A good software should enable creating and managing rule-based alerts and scenarios that flag suspicious activities based on predefined rules and thresholds. These rules can be customized to align with specific regulatory requirements and risk profiles.
3. Risk scoring. A reliable software should assign risk scores to customers and transactions based on their likelihood of involvement in money laundering or fraudulent activities. It should prioritize alerts and investigations based on the assigned risk scores, allowing analysts to focus on high-risk cases.
4. Real-time monitoring and alerts. A good tool will provide real-time monitoring capabilities to detect suspicious activities as they happen.
5. Entity link analysis. The software should offer entity link analysis to uncover connections between customers, accounts, transactions, and other entities. It should help identify complex network patterns and hidden relationships.
6. Visualization and reporting. The software should offer a convenient UI and UX with dashboards and reporting tools to present analysis results in a clear manner. 
7. Flexibility. The software should be flexible and scalable, capable of handling large volumes of data and adapting to changing regulatory requirements.
8. Regulatory compliance support. The software should assist with compliance requirements by incorporating regulatory rules and guidelines. 

FAQ

• What is machine learning in AML?

  Machine learning in anti-money laundering (AML) stands for the use of machine learning algorithms by financial institutions in the detection and prevention of fraud, money laundering, and other financial crimes.

• What is AI in AML?

  Artificial Intelligence in anti-money laundering stands for the use of AI to analyze and detect fraud, money laundering, and other financial crimes.

• How can AI detect money laundering?

  AI systems analyze vast amounts of data in real-time and identify unusual behavioral or transactional patterns that humans may miss.