Accuracy improvement in financial sanction screening: is natural language processing the solution?

Abstract

Sanction screening is a crucial banking compliance process that protects financial institutions from inadvertently engaging with internationally sanctioned individuals or organizations. Given the severe consequences, including financial crime risks and potential loss of banking licenses, effective execution is essential. One of the major challenges in this process is balancing the high rate of false positives, which exceed 90% and lead to inefficiencies due to increased human oversight, with the more critical issue of false negatives, which pose severe regulatory and financial risks by allowing sanctioned entities to go undetected. This study explores the use of Natural Language Processing (NLP) to enhance the accuracy of sanction screening, with a particular focus on reducing false negatives. Using an experimental approach, we evaluated a prototype NLP program on a dataset of sanctioned entities and transactions, assessing its performance in minimising false negatives and understanding its effect on false positives. Our findings demonstrate that while NLP significantly improves sensitivity by detecting more true positives, it also increases false positives, resulting in a trade-off between improved detection and reduced overall accuracy. Given the heightened risks associated with false negatives, this research emphasizes the importance of prioritizing their reduction. The study provides practical insights into how NLP can enhance sanction screening, while recognizing the need for ongoing adaptation to the dynamic nature of the field.