The financial sector, including banks, payment firms, FinTechs, and wealth and asset managers, faces significant challenges in interpreting regulatory guidance and converting it into effective anti-money laundering (AML) strategies.
However, proactive regulators are now working alongside the financial services industry to bridge this gap, leveraging the unique expertise each participant brings to translate regulatory mandates into practical applications within financial institutions’ technology frameworks.
Financial institutions (FIs) often find themselves in a difficult position when trying to meet regulators’ expectations for combatting financial crime. The need for advanced AI solutions to counter new threats and manage large-scale AML operations is clear. However, the complexity involved in developing these models has driven some FIs to rely on generic, off-the-shelf models. These models frequently fall short in terms of customization to specific risk appetites and regulatory requirements for transparency. The so-called ‘black box AI’ can initially show promising results but tends to produce numerous false negatives and ultimately leads to non-compliance.
Napier AI is at the forefront of a compliance-first approach to transaction monitoring. Unlike opaque AI solutions that generate alerts without clarity for AML analysts, Napier AI has developed a comprehensive library of typologies within its transaction monitoring system. These typologies, developed in collaboration with the FCA, are mapped to a data model that allows financial institutions to select and adjust the most relevant typologies according to their risk profiles.
There are three primary methods for detecting AML typologies in transaction monitoring:
- Rule-based detection, which examines individual or aggregated transactions against specific values, frequencies, or thresholds, such as high-value transactions within a set timeframe.
- AI-driven behavioural detection, which analyses transactional behaviour in the context of the entity’s historical activity and peer group norms, such as unusually large withdrawals or deposits.
- AI network analytics, which assess the combination of transactions and behaviours across interconnected entities, identifying patterns like circular payments used by criminals to obscure tracking efforts.
Napier AI’s library features over 100 typologies, each detailing common occurrences, key indicators, and related themes. These themes help link different but related typologies, such as those involving cash usage or cross-border transactions.
As criminal tactics evolve, new technologies are continually employed to evade detection. Napier AI’s global typologies library, integrated with its data model and detection capabilities, enables the company to create a streamlined strategy for addressing prevalent typologies within various financial service segments. This ensures that FIs’ compliance strategies align with global regulatory recommendations.
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