Beyond Speculation Applying AI in Sanctions Compliance

Three years after ChatGPT burst into the public eye, the world is still trying to separate the hype from reality.1 During this time, speculation has often outpaced reality, from predictions of artificial general intelligence (“AGI”) to fears of widespread professional displacement.

Three years on, AGI still feels distant and while large language models (“LLMs”) have certainly transformed the way many professionals work, the displacement of entire professions predicted by many has not yet materialised. In the financial crime compliance space, we have spent the past three years hearing variations of the same questions: “Is AI the silver bullet for financial crime prevention?” and “Will AI replace compliance teams?”.

AI is neither the magic solution for financial crime compliance nor a replacement of human expertise. Instead, it is an enhancer of processes and people. When used well, AI has the potential to make processes more efficient, scalable and consistent, freeing up professionals from repetitive tasks, so they can focus on the hard challenges that require nuanced judgement, accountability and contextual understanding that only humans can deliver.

Just as AI enhances the effectiveness of compliance teams, it will at the same time be exploited by criminals, making its adoption urgent. Moving away from speculation, where can AI be realistically and expertly applied today to strengthen sanctions compliance? Gen AI in Payment Screening One of the big challenges of payment screening is data quality.

Take international payments transmitted over the SWIFT2 network as an example. SWIFT data can be messy. Even though MX messages (ISO 20022)3 are designed to be structured and machine-readable, in practice they often contain unstructured and inconsistent information. Free-text fields are commonly used for remittance details, customer references or narrative descriptions, and depending on how the counterparty enters the data, the quality can vary substantially.

On top of that, optional or repeatable MX fields are used differently across organisations, which makes the data even harder to work with. Traditional screening systems leverage fuzzy matching algorithms and entity resolution to identify potential matches. But poor data quality and ever-increasing regulatory pressure often leads to low screening thresholds, leading to high number of false positives that overwhelms teams and shifts focus away from high-risk and high-value activities.

Generative AI (“GenAI”) can interrupt this chain of poor data quality that leads to low quality alerts. Its strength lies in interpreting messy, unstructured inputs. When applied to payment data (such as SWIFT and SEPA4), GenAI can infer missing context, normalise inconsistent fields and produce structured representations that improve matching accuracy.

By adding this contextual layer, GenAI enables fewer false positives, higher-quality alerts, reduced reliance on low thresholds and more time spent on high-risk cases. The result is a more scalable, human-centred screening process that strengthens both efficiency and investigative quality.

Predictive AI Not all AI is generative AI. Until the GenAI explosion, when people spoke about AI, most were referring to predictive AI. Predictive AI analyses historical data to forecast future events or behaviours. By identifying patterns, trends and correlations, it estimates the likelihood of outcomes such as fraudulent or suspicious transactions.

Unlike generative AI, which creates new content, predictive AI focuses on anticipation and decision support, enabling proactive actions based on data-driven insights. In financial crime, predictive AI is long established. Firms have used it for decades, with fraud prevention and Anti-Money-Laundering transaction monitoring prediction among the classic use cases.