Interpretable Sanctions Screening in Cloud-Native Financial Systems
Keywords:
Cloud-native finance, sanctions screening, explainable AI, feature transparency, false positives .Abstract
Cloud-native technologies enable the optimal use of cloud providers’ services, offering data storage, processing, and compute capabilities that automatically scale according to demand. Government regulations imposed on financial institutions require real-time sanctions screening of customers, transactions, and parties involved in transactions. Screening is often supported by third-party services that read large amounts of data into proprietary databases. Customers and transactions are repeatedly screened against the same lists, producing millions of false positive alerts that require human intervention. Many of these alerts are spurious and non-suspicious. Increasing the efficiency of regulatory workflows using explainable artificial intelligence (AI) is therefore a priority for cloud-native financial institutions.
An AI framework uses machine-learning models that allow interpretations and explanations to satisfy anti-money-laundering specialists. Explainability enhances the quality of the detection model, and the explanations allow compliance, risk, and audit functions to better understand the risk assessment. Training using data generative techniques ensures availability of sufficient samples for the detection scenarios, mitigating the industry challenge of imbalanced datasets with too few samples for model training. Deep neural approaches, supported by embedding techniques that offer a simplified representation of high-dimensional custom datasets, further improve performance.
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