Using Large Language Models For Data Enrichment In Financial Crime and Compliance
Many businesses find themselves under near-constant attack from criminals seeking to commit financial crimes such as fraud, scams, and money laundering.
Though businesses have systems in place to protect against these threats and maintain compliance with the law, these systems are increasingly falling short in detecting the increasingly sophisticated techniques employed by bad actors.
In response, businesses are applying more advanced data and analytics techniques to close their detection gaps by using the data they collect to identify subtle indications of risk or financial crime.
Generative AI and Large Language Models (LLMs) such as GPT-4 are one such innovation that financial crime and compliance teams are looking to leverage.
One of the key use cases is in using LLMs to automatically process long, complex and unstructured text documents, applying techniques such as summarisation and entity extraction to glean key insights from them.
Once these insights are extracted from the text and organised, it then becomes much easier to integrate them into either manual review processes or automated risk systems that monitor individuals and their transactions.
