Practical AI Language Models in Risk and Compliance
A large language model (LLM) toolbox for combating financial crimes
Generic AI chatbots cannot triage alerts, approve payments or remain compliant with FinCEN regulations. Core components of large language models can, when trained and deployed for the domain. In this whitepaper, Quantifind CEO Dr. Ari Tuchman explains two LLM techniques that compliance leaders increasingly hear about, word embeddings and attention, and shows how each applies to anti-money laundering (AML) and Know Your Customer (KYC) work. Domain-specific embeddings define risk typologies more accurately than general-purpose models. Attention mechanisms correctly assign those risks to the people and organizations named in news and regulatory text, so innocent bystanders are not flagged. The paper closes with a look at how the same foundation can streamline Suspicious Activity Report (SAR) narratives.
- General-purpose AI is not built for compliance. An off-the-shelf chatbot cannot evaluate alerts or customers, and any AI must operate inside a comprehensive system of processes and people.
- Domain-specific embeddings close vocabulary gaps. Quantifind’s AML/KYC embeddings cover approximately 50% more financial crime terms than the widely used GloVe embeddings.
- Context determines who carries the risk. Attention-based models separate the subjects of a risk story from the victims, judges and lawyers named alongside them.
- Speed sometimes favors simpler models. For transaction-scale screening, a lighter dependency parser can capture the key contextual signals faster than a full attention model.
The LLM Techniques That Matter for Financial Crime Compliance
Introduction
Why plugging a chatbot into an AML process won’t produce compliant results.
Word Embeddings
How domain-specific training improves coverage and context for financial crime terms.
Attention
How attention mechanisms link risks to the right entities, and when a simpler model is the better choice.
Large Language Models for Risk and Compliance
Unlocking adverse media and other noisy sources with entity-aware risk relevance.
Conclusion
How accurate risk extraction lays the groundwork for automated SAR narratives.
Ari co-founded Quantifind in 2009 and serves as its Chief Executive Officer. As an entrepreneur and atomic physicist, Ari has spent his career developing precision measurement and signal processing techniques for complex, noisy data environments. He has founded multiple companies, raised venture funding, and led technical roadmaps from early research through productization. Ari holds a Ph.D. in Physics from Yale University.
