Traps and Pitfalls: AI Models in Banking Technologies

In the course of the last few years, AI technologies have captured and continue penetrating almost every industry. The positive impact of AI is unquestionable.

AI-based technologies have had a transformative impact on various industries, revolutionizing the way tasks are performed and unlocking new opportunities for growth and innovation. AI has enhanced efficiency, accuracy, and productivity across sectors, from automating repetitive processes to enabling predictive analytics and personalized recommendations. AI has also fueled breakthroughs in finance, compliance, and other associated fields, enabling faster risk assessments and efficient resource allocation. AI-based technologies have ushered in a new era of possibilities, empowering organizations to achieve higher levels of performance and positively impacting numerous aspects of the modern workplace.

However, AI can face challenges when it comes to AML compliance. It can lead to more false alerts and missed hits. It often also becomes the cause of AML discrimination. It’s in addition to the fact, that using AI models for sanction screening cannot, but the very nature of AI, meet the regulatory requirement of traceability and explainability.

Why does it happen:

Lack of contextual understanding

AI models may struggle to understand the full context of a transaction or the subtleties of complex money laundering schemes. AML screening often requires deep knowledge of financial regulations, patterns of illicit activity, and evolving techniques used by money launderers. AI may not possess the necessary contextual understanding of each of the factors involved in the scheme to accurately detect suspicious activities.

Limited training data

AI models rely on large amounts of high-quality training data to make accurate predictions. However, obtaining labeled data for money laundering activities can be challenging due to the sensitive and secretive nature of such activities. This limited data can result in AI models not being adequately trained to recognize new or evolving money laundering patterns, leading to both false alerts and missed hits.

Bias and False Positives

AI models are susceptible to bias in their training data or the design of their algorithms. If historical data used to train an AI model is biased, it can result in disproportionate false alerts for certain groups or patterns that are overrepresented in the training set. This can lead to an increased number of false positives, where legitimate transactions are flagged as suspicious, causing unnecessary disruptions and delays.

Rapidly evolving criminal techniques

Money laundering techniques constantly evolve to bypass detection systems. AI models, which are trained on historical data, may struggle to keep up with these dynamic changes. Criminals may intentionally design transactions to evade AI detection by exploiting weaknesses or blind spots in the model’s algorithms, leading to missed hits or false negatives. Moreover, they may use AI capabilities to trick itself into finding these loopholes for them. It’s tricky, but possible.

Interpretability and explainability

AI models, especially complex ones like deep learning neural networks, lack transparency and interpretability. It becomes challenging to understand why the AI system flagged a particular transaction as suspicious or missed a money laundering case. This lack of explainability hinders effective auditing, accountability, and regulatory compliance.

In today’s regulatory regime, the regulated institutions are required to provide a clear audit trail for all their AML screening decisions, which becomes impossible when an AI system is deployed.

Using strictly mathematical models and algorithms based on mathematics can have advantages over relying solely on AI for AML sanction screening.

Here is why:


Mathematical models often have a clear and well-defined set of rules and calculations. They operate based on known mathematical principles and can be easily understood and audited by human analysts, regulators, and stakeholders. This transparency helps build trust and confidence in the screening process.

Regulatory compliance

AML sanction screening is heavily regulated, and strict adherence to regulatory guidelines is crucial. Mathematical models can be designed to ensure compliance with specific legal requirements, making it easier to demonstrate adherence to regulations. Additionally, mathematical models can be audited and verified more easily to ensure compliance, which may be challenging with complex AI algorithms.

Predictability and stability

Mathematical models tend to be more stable and predictable compared to AI models. Once established, they can provide consistent results over time, minimizing the risk of unexpected fluctuations or false positives/negatives due to changing AI training data or algorithms. This stability is important in maintaining the integrity of the screening process.

Interpretability and accountability

Mathematical models are more interpretable and provide a clear explanation of how they arrived at a particular decision. This interpretability allows human analysts to understand why a transaction was flagged or not flagged, enabling them to validate and explain the screening outcomes. Moreover, the accountability for decision-making lies with the explicitly defined rules rather than complex AI algorithms, making it easier to justify and defend decisions if necessary.

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