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4 Essential AML Use Cases for AI Technology

4 Essential AML Use Cases for AI Technology

The fight against financial crime is an arms race. Banks of all shapes and sizes need to use artificial intelligence (AI) technology to improve their anti-money laundering (AML) programs. However, not all AI is created equal; different AI technologies work better for different use cases. Built-for-purpose AI technology helps banks identify suspicious activity in real-time, at scale. This technology supports a risk-based approach, allowing banks to detect financial crime more effectively and efficiently, prevent costly enforcement actions, and protect their customers and reputation.

In this article we will discuss the following use cases for AI technology, how they work in an AML context, and why AML professionals need them:

  1. Anomaly Detection 
  2. Pattern Detection 
  3. False Positive Reduction 
  4. pKYC

1. Anomaly Detection 

How AI Helps Banks with Anomaly Detection

Anomaly detection uses multiple machine learning technologies, such as isolation forest algorithms and neural networks, to identify unknown suspicious behavior in transaction datasets. Anomaly detection is a “safety net” that can detect all kinds of complex criminal behavior that rule-based technology often misses. Anomaly detection technologies are highly adaptable, learning from investigator input to target desired results. This technology rapidly analyzes massive datasets to surface otherwise unidentifiable suspicious activity.

The Benefits of AI Anomaly Detection for Banking AML

With growing transaction counts, banks can’t hire enough human AML investigators to sufficiently scrutinize customer behavior for money laundering activity. Anomaly detection automates the grunt work of looking for suspicious activity, finding new types of scams and money-laundering typologies. This frees up AML professionals’ time for the more important work of investigating and building case narratives. Focusing resources and attention to areas with a higher likelihood of money laundering promotes the risk-based approach regulators require. This helps banks achieve their regulatory, reputational, and social goals.

2. Pattern Recognition 

How AI Helps Banks with Pattern Recognition

Pattern recognition requires AI models that excel at detecting specific known criminal patterns. These models automate the identification of complex patterns that may go unnoticed through traditional manual or rule-based methods. We train AI models with labelled data, and the models identify targeted patterns rapidly and at scale. In an AML context, designing AI models with specific subject matter expertise is crucial to the models’ effectiveness. 

Generative AI, popularized by Chat GPT and other Large Language Models (LLMs), has valuable AML applications. Large Transaction Models (LTMs), a Generative AI technology pioneered by Hawk AI, enhance pattern recognition by connecting behavior across massive datasets. LTMs can process and analyze sparsely labeled data in a scalable and precise manner. These LTMs excel at long range dependencies, which means that they find patterns across long “distances” within a dataset. If machine learning-based models can effectively analyze a paragraph, an LTM can do so to an entire novel. 

“LTMs solve a ‘needle in the haystack’ problem,” said Felix Berkhahn, Chief Data Scientist at Hawk AI. “They are even better than other models at identifying unknown patterns in the data.”

The Benefits of AI Pattern Recognition for Banking AML 

AI technologies reduce transaction noise and focus AML teams’ attention on relevant and productive information. This technology improves investigators’ ability to identify suspicious patterns, enhancing the effectiveness of banks’ AML programs. The process improvements this technology enables will support your bank’s risk-based AML approach and help achieve key organizational goals.

3. False Positive Reduction 

How AI Helps Banks with False Positive Reduction 

AI-powered false positive reduction minimizes the number of incorrectly flagged alerts for AML teams to review. We can use advanced machine learning algorithms to create models of expected transaction behavior, giving investigators more precise definitions of normal and abnormal activity. When a rule triggers an alert, a false positive reduction model uses contextual information from the dataset to determine whether the activity warrants further examination. The model also automatically creates an audit trail for every alert, using multiple risk factors to explain why it labelled the alert as a false positive or not.

The Benefits of AI False Positive Reduction for Banking AML

AI false positive reduction technology refines the money laundering detection process, reducing the burden on AML compliance teams and allowing them to focus on genuine risks. The explainability of AI false positive reduction technology promotes the risk-based approach required by regulators, providing audit trails for regulatory examinations. In sum, AI-powered false positive reduction enhances the efficiency and effectiveness of AML teams, helping them achieve their organizational goals. 

4. pKYC 

How AI Helps Banks with pKYC 

Perpetual Know Your Customer, or pKYC, is becoming a critical element in reducing AML risk exposure. This technology can use dynamic AI-derived factors, i.e., the suspicious behavior detected by AI models, to generate more accurate and up-to-date risk assignments. Without the precise view of customer risk AI technology provides, pKYC is practically impossible. 

The Benefits of AI pKYC for Banking AML 

Customer risk profiles can change in a heartbeat. AI-powered pKYC technology allows AML teams to better monitor customer behavior for these changes and maintain a healthy risk portfolio. This empowers banks to employ a risk-based approach to complying with AML regulations, protecting their reputation, and stopping financial crime. Some believe that regulation requiring pKYC processes is on the horizon; this would make AI technology even more essential.

“AI-powered pKYC is more important now than ever,” said Berkhahn. “Banks need to update customer risk profiles as quickly and efficiently as possible, and AI technology allows them to do that.”

Hawk AI’s AML Technology

Hawk AI has designed our AML platform for Anomaly Detection, Pattern Detection, False Positive Reduction, and pKYC use cases, with state-of-the art AI technology at the core. This technology helps banks make their AML programs more efficient and effective, deliver desired results, and achieve AML compliance goals. 

To learn more, request a demo today.

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