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

Area of Science:

• Computational Linguistics
• Financial Compliance Technology
• Artificial Intelligence in Finance

Background:

• Sanction screening is vital for banking compliance, preventing financial crime and license loss.
• High false positive rates (over 90%) cause inefficiencies, while false negatives pose significant regulatory risks.
• Existing screening methods struggle to balance detection accuracy with operational efficiency.

Purpose of the Study:

• To investigate the efficacy of Natural Language Processing (NLP) in enhancing sanction screening accuracy.
• To specifically evaluate NLP's performance in minimizing false negatives within financial transactions.
• To understand the impact of NLP implementation on both false negatives and false positives.

Main Methods:

• An experimental approach was employed to assess a prototype NLP program.
• The NLP program was evaluated on a dataset comprising sanctioned entities and financial transactions.
• Performance metrics focused on the reduction of false negatives and the alteration of false positive rates.

Main Results:

• NLP significantly improved detection rates by identifying more true positives, thus enhancing sensitivity.
• The implementation of NLP led to an increase in the rate of false positives.
• A clear trade-off was observed between improved detection of sanctioned entities and overall screening accuracy.

Conclusions:

• Natural Language Processing offers a promising avenue for improving sanction screening, particularly in reducing critical false negatives.
• The study highlights the necessity of managing the increase in false positives associated with NLP adoption.
• Continuous adaptation of NLP models is crucial to address the evolving landscape of financial sanctions.