While transformative, AI’s integration into compliance workflows has resulted in some questions around security, explainability, data integrity, and accuracy. Financial landscapes and the data they are built on are sensitive, and before fully adopting any new technologies, firms need clear information and documentation upfront to ensure they’re trustworthy and will work as expected.
To address these concerns and unpack how AI can enhance communications monitoring and resolve frequent surveillance challenges, we have put together a video series where Global Relay specialists clarify how large language models (LLM) detect risk while ensuring high levels of data security.
LLM training
Lexicon-based and small-scale AI models will require frequent training and updates to understand all the risk areas and keywords they need to monitor for. LLMs are already built with a firm foundation of data, context, and knowledge needed to identify risk.
LLMs don’t need to be trained from scratch, they only need to be fine-tuned to align with firms’ specific risk profile. This means LLMs can recognize vital context, sentiment, and language, making them more able to accurately spot and flag risky communications.
LLM risk justification
LLMs ability to recognize sentiment and intent means it can evaluate messages and their full context to justify whether it may contain risk. Where a lexicon-based solution will flag any message that matches prescribed keywords lists, LLMs can study and analyze a message against a series of more comprehensive risk indicators.
When an LLM does spot risk, it will flag a message as suspicious, alert reviewers, and give an explanation as to why it concluded that this message was risky. This creates smarter alerts for compliance teams and helps focus on true positives, as well as saving valuable time by streamlining the risk review process.
Data security and AI
Data security is a core concern surrounding AI model use, especially when it comes to the protection of sensitive and private financial information. To ensure complete safety and security, Global Relay uses synthetic data that’s modeled after real-world risks to develop our surveillance models. Client information is never used to train these models or covertly stored and accessed – it remains protected in private data centers at all times.
LLM transparency
Transparency and clear vendor documentation are key when it comes to AI-enabled surveillance models. To provide complete oversight, we document all the steps that our model goes through to be trained, tested, and validated. In addition, we ensure that everything remains in line with regulations on the governance of AI use in compliance and surveillance.
