Using LLMs to Catch Money Laundering: A Case Study

Enterprises are starting to use Large Language Models (LLMs) but we're still in the early days. LLMs can be used in complex scenarios like anti-money laundering (AML) contexts where sensitive data needs to be handled with care, dealing with technical issues such as data integration, prompt injections, permissions, and auditability. In an AML context, LLMs can screen customers to ensure they're not high-risk businesses, but this process requires careful consideration of how to represent data properly, including classifying fields as "Text" or "Metadata", and managing permissions and access controls. The use of LLMs in regulated industries also raises security concerns such as prompt injections, which can be exploited by malicious actors. However, with the right guardrails and policies in place, enterprises can harness the power of LLMs to drive decision-making and safeguard operations, gaining an advantage over their competitors.

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