From Noise to Knowledge: Contextualizing risk in financial communications with GenAI 

Modern financial firms need to identify and surface risks when monitoring communications quickly. As firms increasingly turn to GenAI to bolster their risk management strategies, a new question emerges—how well does a GenAI model understand the nuanced context of your firm’s communications? 

More importantly, how well does a GenAI vendor understand the need to offer context behind a model’s output and accordingly tailor its development practices? 

Communication is complex and firms need models that can distinguish between casual conversation and potential red flags, understand the subtle differences in communication across various financial markets, and adapt to the ever-evolving language of finance. 

Context is everything when it comes to AI-driven communications monitoring and surveillance. Technology is reshaping the landscape of financial compliance and risk detection with a layered approach to building context-aware AI models. One thing is becoming very clear—the future of surfacing risk with GenAI lies in understanding data better and offering firms the flexibility to understand context of their outputs. 

The significance of context in GenAI for financial institutions 

Context is essential to understanding any form of communication—financial or not. Often, seemingly common phrases can have very different implications depending on the context. 

For example, the phrase “I really need a favor” might be innocuous in most situations, but in the context of a cross-border deal or in a market where the exchange of favors is less common than regulation might permit, it could be a significant red flag. 

Specialized jargon and market-specific language make a model’s task more challenging. For instance, in equities markets, sharing MNPI is strictly forbidden, while in energy markets, discussions about utilities or potential delivery delays are more common and not necessarily indicative of wrongdoing. 

Firm-based differences add another layer of complexity. For example, the way traders at one bank communicate might differ subtly from their counterparts in your bank. GenAI models need to be sophisticated enough to recognize these nuances without overemphasizing them or creating false positives based on regional or firm-based speech patterns. 

Furthermore, what one institution considers a potentially risky communication might be viewed differently by another, depending on its specific risk appetite and regulatory obligations. This is especially true with conduct-related issues where, at a glance, workplace complaints can take on more significance depending on a firm’s history with culture issues.  

And if these challenges weren’t enough, GenAI vendors must also account for multilingual  

communication when developing models. The model must not only accurately translate the content but also understand idioms, cultural references, and context-specific meanings that may not have direct equivalents in other languages. 

To address these concerns, GenAI vendors must offer firms the flexibility to define their own risk boundaries in output and fine-tune their models. 

One benefit of imposing more context on outputs is the dramatic reduction in false positives and noise. By distinguishing between genuinely suspicious activity and normal business operations, context-aware AI allows compliance teams to focus their efforts on real risks rather than wading through a sea of irrelevant alerts. 

Hand in hand with noise reduction comes an improvement in the relevance of generated alerts.  

When an AI system flags a communication, you can have greater confidence that it truly warrants attention. This improved precision stems from the model’s ability to understand the context of conversations, including market-specific jargon, regional language differences, and the subtle cues that might indicate potential risks. 

Perhaps most importantly, the ability to fine-tune model sensitivity allows you to strike the right balance between comprehensive coverage and operational efficiency. This customization ensures that the monitoring system aligns perfectly with your risk taxonomy and regulatory obligations. 

Having said all that, what does context-aware model development look like? 

A layered approach to building context into models 

A best practice for developing context-aware AI models in financial communications monitoring involves a 3-layered approach. This method provides a comprehensive framework for understanding and contextualizing communications, offering a more nuanced and accurate risk detection system. 

1. The first layer involves ingesting messages, classifying, and tagging them. This layer doesn’t generate alerts; it simply tags and classifies the incoming data by looking at contextual information. 

2. The second layer aggregates the information tagged in the first layer against specific risks. For example, it might consider whether secrecy language appears alongside specific trade talk or bragging (for instance). 

3. The third layer uses GenAI to perform a comprehensive analysis. It can identify potential issues that the more targeted approaches of the first two layers might have missed. 

This 3-layered approach offers a level of flexibility and customization that’s crucial for financial firms. Unlike a one-size-fits-all model, the layered approach allows for more precise risk detection and reduced noise in alerts. 

One key benefit is the ability to provide rich context around why something is flagged as a potential risk. This detailed context helps compliance teams understand not just that a risk was detected, but why it was flagged, enabling more informed decision-making. 

The layered approach also allows for more nimble adaptation to different markets and communication styles. For instance, a model can differentiate how language is used in various contexts, such as the implications of “asking for a favor” in different markets. 

The 3-layered approach also helps firms tailor model outputs to their risk appetites. This customization allows you to adjust the thresholds for risk detection based on your unique requirements. An added benefit is that you can adapt to different regulatory environments and internal risk taxonomies without retraining models. 

Of course, much depends on the quality and relevance of the training data used. Specialized data helps the model understand context-specific risks that might not be apparent in general language models. However, for some models, such as ones focused on detecting secrecy, using open-source datasets with finance-specific prompts is the right approach. 

This balanced approach allows for the development of robust models without the need for extensive, hard-to-obtain financial datasets for every aspect of the system. It also enables the model to understand general language patterns while still being attuned to finance-specific nuances. Contextualizing risk is an ongoing process that requires continuous refinement. It requires a partnership between you and your GenAI providers to continually optimize your models based on real-world performance and evolving risk landscapes. 

Context is key to surfacing risk 

The importance of context in AI-driven communications monitoring is paramount, particularly in an industry where a single misinterpreted message could have significant regulatory or financial repercussions. 

Shield’s approach to this challenge exemplifies best practices in the field: 

• Our 3-layered approach, combining initial classification, risk-specific aggregation, and comprehensive GenAI analysis, offers a robust solution to the intricacies of financial communication monitoring. 

• Model flexibility that helps you define risk thresholds. 

• Customization to your specific needs and maintaining an ongoing refinement process. 

When paired with model flexibility and a commitment to transparency, defining context to aid model output reduces false positives and lifts your surveillance program to new heights. 

Learn how AmplifAI—Shield’s GenAI toolkit—surfaces risks and offers unmatched context in model outputs.