The use of Artificial Intelligence (AI) and machine learning (ML) models in transaction monitoring has become invaluable in strengthening internal controls and responding to fast-evolving financial crime patterns. The variety of the in-depth insights and pattern detection methodologies used in these models makes up for the shortcomings exhibited by traditional systems, whilst simultaneously discovering behaviours and irregularities that were previously overlooked, making this technical advancement crucial in the evolution of financial crime compliance.
Unquestionably, the exploitation of certain AI and ML models allows organisations to enhance their transaction monitoring capabilities, improve efficiency and stay ahead of money laundering and terrorist financing threats. That being said, the output of information is only as good as the technology processing the inputted material, meaning that the AI must be tested regularly to ensure the quality, security, and cost-effectiveness of the systems being used.
AI in transaction monitoring: Challenges & solutions
The evolution of AI and ML models simultaneously increases their complexity, bringing forth issues such as the “black box” problem; whilst the models themselves can provide answers, the rationale behind the same is not evident. Not understanding, and consequently not being able to explain the decision-making process of your organisation’s transaction monitoring system, raises concerns since this would imply a lack of transparency and trust in the system being used. Moreover, regulators would require an explanation behind the models’ logic to ascertain compliance with the relevant legal and regulatory requirements.
It is thus crucial for organisations to understand and be able to provide clear explanations about how and why specific decisions were made by the AI and ML models, both for regulatory compliance requirements, as well as to improve the models’ efficiency and performance.
Coupled with the explainability issue, it is rather easy for biases to be created, and subsequently cause certain clients to be unfairly targeted when using AI models. Fairness and compliance with regulations are part and parcel of these procedures – especially within regulated sectors – and a potential lack of transparency in decision-making processes makes this difficult to achieve.
Mitigating the risk of recurring bias is essential. Vigorous bias detection and correction mechanisms are key solutions which ought to be implemented, whilst the process of reviewing AI models and their outputs is also necessary for fine-tuning existing models and their use.
It may be the case that AI algorithms produce outputs which are not based on specific data, or which do not follow any identifiable patterns, greatly increasing performance risk. Too much reliance on AI could backfire, resulting in incorrect data which leaves entities liable to legal and regulatory repercussions.
This obstacle could be tackled through the regular testing and subsequent improvement of the systems used, to ensure that the transactions which warrant further scrutiny are being flagged for review. Implementing rigorous verification processes, such as two-factor authentication methods, higher quality datasets, human default checking and encouraging model transparency, as well as constantly investing in furthering general AI literacy, are also key when using such models.
How can we assist?
Relying solely on transaction monitoring systems just because they use AI and ML isn’t enough. To effectively manage these models, it’s crucial to understand and explain how they work. This step isn’t just beneficial, it’s essential. Our Financial Crime Compliance team is here to support you in selecting a clear solution, testing your transaction monitoring systems, and developing or maintaining AI-focused guidelines and procedures. These are tailored to ensure you fully harness the benefits of AI while continuously enhancing your transaction monitoring capabilities.
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