Machine Learning in AML: Ongoing Monitoring that Learns and Adapt

Between speed, scale, and reasons, AML teams can no longer trade one for the other. Machine learning detects what static rules miss, still without evidence and explainability, even accurate alerts cause a delay in review. Embedding source-level proof and explainable AI into every decision restores accountability while keeping pace with compliance demands.

As compliance officers and regulators want reasons for every flagged entity, machine learning poses a challenge; it’s often like a black box that identifies suspicious patterns but doesn’t give reasoning.

One of the concerns of institutions considering adopting AI for AML compliance is:

How can financial institutions (FIs) utilize machine learning without compromising on transparency and accountability, which is a major requirement of regulators?

The answer is: FIs can integrate machine learning in AML without compromising on transparency and accountability by adopting source-based evidence and traceable risk alerts, along with the adoption of Explainable AI approaches that address the non-transparent nature of black-box scoring.

Why is Machine Learning Non-Negotiable for AML Tools?

AML’s inherently complex nature sets it apart from many other regulatory areas. With the constant evolution of laws, the financial industry has emerged as the most heavily regulated one. This continuous shifting is exemplified by the FATF’s regular updates of recommendations and guidance, with a recent emphasis on virtual assets and beneficial ownership. This implies that adhering to a complex regulatory realm that went through sudden shifts in regulations turns out to be a big challenge for FIs.

Additionally, the complexity is doubled by the fact that legislation and requirements vary across different jurisdictions. Banks that are working globally and have operations spanning dozens of countries with different AML requirements. This is a huge challenge for centralized AML operations and data management. Financial institutions often struggle due to the lack of standardized data standards across regulators.

The power of accurate data in Machine Learning can’t be undermined. Recent studies emphasize that data plays a significant role, especially in contributing to the false positives in AML alerts, which cost organizations $12.9 million annually.
This is where machine learning comes into action; it is ideal for combating financial crime in vast datasets, detecting anomalies and patterns that humans or conventional rules often miss.

Limitations of Rules-Based Systems Vs. Advanced Machine Learning Systems

Conventional rule-based systems are inadequate for handling the intricate nature and massive scale of advanced financial transactions. Research indicates that rule-based systems can generate approximately 95% or higher false positives, meaning that most alerts are not genuine money laundering. Such limitations result in alert fatigue among the compliance officers, as well as allow the criminals to pass effortlessly through the systems.

Whereas AML machine learning is incredibly powerful as it identifies patterns in real-time. However, models utilizing black box approaches are not sufficient as they don’t give reasons for their identifications. Therefore, in this case, explainable AI takes the place.

Now, the question that arises here is: What is Explainable AI (XAI)? It is a set of strategies that makes the AI decisions transparent, assists humans to understand, trust, and effectively manage these systems. XAI is based on three core principles:

• Interpretability (Comprehending how a model made a decision)

• Accountability (The ability to justify its choices)

• Transparency (Openness about the model’s inner workings)

XAI directly solves the black box issue in the advanced ML models, which obscures the reasoning behind their decisions.

How XAI Techniques are Utilized in AML Machine Learning Systems?

There are some explainable AI strategies used in anti-money laundering machine learning systems that assist them in providing reasons to individuals and avoid being a black box. These techniques are:

• LIME (Local Interpretable Model-Agnostic Explanations)

LIME assists the AML machine learning systems in comprehending the reasons behind the flagging of a specific transaction. For instance, it might provide the following reasons for a single alert like “transaction to a high-risk jurisdiction”, “unusual transaction amount”, or “new counterparty”. That’s how the regulators or analysts can easily understand the underlying reasons with the right machine learning transaction monitoring systems.

• SHAP (Shapley Additive Explanations)

SHAP is a technique by which investigators receive different clues relevant to the suspicious activity. For instance, if a detector is trying to figure out why the AML machine learning system flags the particular individual as suspicious. The system will provide the investigator with the following clues:

• What’s the profession of the individual?
• How much money does the individual have?
• Who are the people with whom the individual is connected?
• Where is the individual traveling to?

All these clues prove that the specific individual is somewhat suspicious and needs proper investigation.

• Simpler Feature Importance

Simpler feature importance assists in comprehending the overall risk assessment factors. It generally works as a report card, explaining which prediction is more valid and detecting influential data points in risk assessment.

How to Make Machine Learning in AML More Cost-Effective?

Here is a list of some AML machine learning techniques that are way more cost-effective:

• Context-Driven Data

With context-driven data, machine learning powered tools can go beyond basic monitoring. It reduces the rate of false positives by enriching the simple transaction data with some specific details, such as the industry-relevant risks, previous customer behaviour, or global events.

• Strategic Data Labeling

Effective data labeling is essential for identifying new fraud patterns, yet it’s an expensive technique. Therefore, financial institutions must balance the cost of data labeling with its pros by considering the use of semi-supervised learning and anomaly detection for known patterns to cut costs.

• Active Learning and Human-in-the-loop

Active learning and human-in-the-loop techniques optimize the AML machine learning process, where the professionals are directed to uncertain information to reduce the amount of data needing manual labels.

Choose AML Watcher for Transparent Compliance and Reduced Costs

Tired of false alerts and identifying patterns without knowing the underlying reasons? Conventional AML tires your energies, and current ML often works like a black box, lacking transparency for the investigators.

Here’s how AML Watcher establishes trust:

• Deep Contextual Data: Get data of comprehensive sanctions lists such as OFAC, UN, UK, World Bank, and FATF, adverse media monitoring, and other essential insights for precise screening across all entities or transactions.
• Explainable AI: Observe the reasons behind the flagging of alerts, and reduce the black box thing by creating transparency for the investigators.
• Proactive and Accurate Data: Gain accurate data in real-time and reduce the rate of false positives. Perform ongoing monitoring that adapts to evolving threats.

Intelligent Risk Assessment:  Create risk-based transparency by emphasizing actions with intelligent risk assessment. Direct time and effort to where the business needs.