AI technology and AML compliance applications

Melanie Markay, Kristina French, Alex Rubin
| 2/26/2024
AI technology and AML compliance applications

Incorporating AI technology in AML compliance programs can enhance processes if done carefully.

Artificial intelligence (AI) technology has substantial potential to enhance processes and workflows, allowing humans to focus on higher-value work. However, the complexity of deploying AI can lead to uncertainty regarding where and how best to apply it, especially in highly regulated areas such as anti-money laundering (AML). By considering how AI technology could fit into day-to-day operations and by understanding the risks and implications, financial services organizations can enhance existing AML processes while maintaining compliance.

Keep informed
Sign up to receive the latest insights on strengthening your financial crime program.

What is artificial intelligence?

At its core, AI technology enables machines to mimic human intelligence. Human intelligence is composed of countless experiences and sensory inputs. AI can imitate intelligent human behavior when computers are exposed to millions of pieces of data. Machines learn to recognize patterns and develop conclusions, which empowers them to perform tasks that previously required human intelligence.

AI technology comes in various forms, but two use cases tend to be the most prolific: machine learning and natural language processing (NLP).

Machine learning

The central focus of machine learning is problem-solving. Computers process and analyze data on a large scale and can generate predictive modeling and decision-making. When supplied with large quantities of high-quality data, machine learning models can drill into underlying trends to make accurate predictions.

Natural language processing

The primary focus of NLP models is language comprehension. Just like a human learns to read, a machine can begin to read language if exposed to enough words and sentences to generate meaning. The ability of a large language model, such as ChatGPT, to comprehend multiple languages is a prime example of what NLP models can achieve.

Practical AML compliance applications

AI technology can help AML professionals maximize their day-to-day efficiency, and it can help set up financial services organizations for future success with automation. Some use cases for AI in AML compliance operations include:

Transaction monitoring

Generally, technology and crime outpace regulation. Criminals come up with new ways to commit fraud and launder money that include new financial instruments such as virtual and digital assets to facilitate their activities. AI-assisted transaction monitoring could potentially augment or replace threshold rule systems and add the capability of using a financial services organization’s vast amount of historical data to make accurate decisions when flagging transactions.

AML investigators often spend significant time analyzing false positive alerts. Incorporating AI capabilities into transaction monitoring systems can help financial services organizations analyze transaction data and identify patterns of suspicious activity. An AI model implemented within transaction monitoring can help to prioritize or potentially dismiss alerts before human capital is spent.

AI-powered systems can analyze vast amounts of transaction data in real time, allowing organizations to efficiently tune their monitoring parameters. By continually learning from historical data and patterns, AI algorithms can adapt and refine transaction monitoring rules, providing for a more precise and responsive detection of potential illicit activities. Moreover, the ability of an AI system to recognize complex patterns and anomalies can help distinguish between genuine threats and false positives with a high degree of efficiency. Financial services organizations could then significantly reduce the burden of reviewing and investigating false alerts, allowing compliance teams to focus on more complex and high-risk cases.

Case management

AML investigators spend time organizing cases for review by the appropriate analyst according to case type and prioritization. Intelligent learning models can automate case classification, prioritization, and assignment processes by analyzing data and historical patterns before routing cases to the most appropriate teams or individuals.

AI case management systems can also provide predictive analytics and support decision-making by suggesting potential outcomes and strategies based on historical data. Such analysis expedites alert or case resolution and enhances accuracy, consistency, and the ability to derive actionable insights from the information within each alert or case, ultimately leading to more effective and informed decision-making.

Managers can be better equipped to handle process inefficiencies when using AI-powered analytical systems integrated with transaction monitoring. Instead of manually building out analytical dashboards and spreadsheets, AI systems can do the heavy lifting and leave the critical decision-making to humans.

Suspicious activity reporting

AI technology can enhance suspicious activity reporting (SAR) by handling data input and freeing up human intellectual bandwidth to focus on understanding, documenting, and reporting the underlying suspicious activity. Manually inputting data often leads to errors, but AI technology can extract data directly from the appropriate source and handle data input, potentially increasing accuracy and reducing overall completion time.

Financial services organizations rely on analysts to assess complex money laundering typologies. Organizations routinely file SARs that do not require a complex assessment, such as for structuring activity or merchants with high dispute rates indicative of fraud. AI tools can autofill reports on cases with simpler typologies for quick review by an analyst. For cases requiring a more complex investigation, NLP can provide report writers with quick phrasing and word choice suggestions. These systems can be implemented internally and tailored to AML compliance writing to mitigate any data security concerns with open source processors.

NLP already assists writers with grammar and spelling, enhancing overall compliance reporting quality. AI-powered writing tools can help organizations overcome linguistic and geographic barriers, especially within outsourced review services. Analysts can then catch mistakes up front, minimizing time spent correcting errors and leading to more efficient suspicious activity reporting.

Risks and controls

Clearly, various practical use cases have the potential to significantly increase efficiency for AML compliance programs. However, organizations should be aware of potential risks and take steps to implement sufficient controls. Following are several factors to consider prior to implementing AI technology:

Explainability and transparency

Some AI models obscure their inner workings and are not readily interpretable by humans. The vagueness of AI decision-making processes can pose significant challenges for financial services organizations to rationalize model outputs to regulatory agencies and internal stakeholders. Organizations unable to explain why a particular transaction was flagged as suspicious – or not – or why certain customers were classified as high risk could find themselves in a precarious position.

Financial services organizations should develop robust model risk management methodologies to respond to model transparency and documentation needs. These methodologies can encompass a range of strategies, from adopting simpler, more interpretable models to employing specialized tools designed for model interpretation. By embracing simpler models such as decision trees, financial services organizations can provide clearer explanations for model outputs. Documenting the entire model-building process, from data preprocessing to architecture selection, is essential for providing context for stakeholders as they seek to comprehend model decisions. Thorough and well-documented models enhance regulatory compliance and facilitate effective model management and risk mitigation.

Data quality and bias

The quality of data used to train AI models directly affects model accuracy and reliability. Poor data quality, such as inaccuracies, incompleteness, or outdated information, can lead to erroneous conclusions and unidentified suspicious activities. Bias in the data used for training AI-driven AML models can perpetuate discriminatory outcomes, as AI models can inadvertently learn and amplify existing biases.

Financial services organizations can adopt several proactive measures to mitigate risks associated with data quality and bias. Some measures include:

  • Thoroughly evaluating AI technology vendors’ track records, reputations, and commitments to data quality and fairness
  • Performing a quality assessment of the data used by AI models to independently confirm the quality of the training data
  • Conducting thorough, regular assessments to uncover any hidden biases within data sets

By continually monitoring and enhancing data quality and addressing biases, financial services organizations can strengthen the reliability of AI-driven AML models while bolstering their overall compliance efforts.

Model validation and monitoring

Regular model validation and ongoing monitoring of AI-driven AML models are two critical practices that help verify the continuing accuracy, reliability, and compliance of such models. Transactional activity and money laundering tactics constantly evolve. Models can quickly become outdated without regular validation, leading to unidentified suspicious activities or to a reduction in the value provided by the model.

Through periodic validation, financial services organizations can adapt their models to new patterns and emerging threats, enhancing their effectiveness in detecting illicit financial activities. Regular validations serve to fine-tune model parameters, and they present an opportunity to retrain models with updated data, verifying they remain aligned with the current financial landscape dynamics. Through periodic validation, financial services organizations can adapt their models to new patterns and emerging threats, enhancing their effectiveness in detecting illicit financial activities. Regular validations serve to fine-tune model parameters, and they present an opportunity to retrain models with updated data, verifying they remain aligned with the current financial landscape dynamics.

Furthermore, ongoing monitoring of AI-driven AML models enables financial services organizations to proactively identify any deviations from expected model performance and swiftly mitigate potential issues. This continual improvement cycle safeguards against missed suspicious activities and bolsters overall risk management strategies, helping organizations stay ahead in the ever-evolving battle against financial crime while maintaining regulatory compliance and operational excellence.

Model security and governance

Malicious actors could exploit vulnerabilities in an AI model, compromising its integrity and accuracy. Financial services organizations should implement stringent security measures such as access controls and continuous monitoring to protect the model and sensitive data analyzed. Regular security assessments and threat modeling exercises are also essential to proactively identify and address vulnerabilities.

Model governance helps confirm that AI-driven AML models align with an organization’s objectives and ethical standards. The lack of proper governance can lead to model biases, discriminatory outcomes, and noncompliance with regulatory standards, posing legal and reputational risks. To mitigate these risks, organizations should establish clear roles and responsibilities for model oversight, implement transparent guidelines for model development, and maintain an audit trail of model decisions.

AML compliance and AI technology

The future of AML compliance likely will include the practical use of AI technology, and it is important for financial services organizations to understand how they might benefit from such technology while maintaining AML compliance. AI could drastically improve AML processes, but financial services organizations must proceed with caution when implementing AI technology. By developing robust model risk management practices and methodologies to understand their models more fully and by confirming that controls exist to verify models are producing appropriate output, financial services organizations could integrate the use of AI technology to enhance the strength and efficacy of their AML compliance programs.