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Open Books and AI Vendors: Transparency’s Role in Model Validation

As financial services firms have adopted artificial intelligence (AI) for compliance and surveillance, the rapid rise of GenAI in other industries has raised questions of its potential in kind. In turn, these developments have led to vendors selling sophisticated LLM and GenAI models to banks. 

While this shift is promising for overall AI development, it raises a critical question—how can you ensure that a vendor’s model outputs are properly validated? 

In the past, models used pre-defined datasets, making explainability and verification relatively straightforward. However, newer GenAI models are often trained on a vendor’s proprietary datasets, making output validation a complex process. 

Since vendors are unlikely to grant a firm access to proprietary data, examining its model validation processes is critical. After all, the stakes are too high to base decisions on the outputs of a model whose validation process is questionable. 

The importance of validated model results 

As AI’s potential to help compliance and surveillance within firms increases, the risks associated with poorly validated models loom large, especially when reviewing regulatory, business, and reputational concerns. 

The regulatory landscape for AI in finance is evolving quickly. Regulators like FINRA and FCA have issued guidelines around GenAI usage in compliance. The EU AI Act offers the most comprehensive set of guidelines for firms to follow. As regulators look to compliantly frame AI development, model outputs will come under greater scrutiny, making poorly validated results unacceptable. These pressures underscore the need for robust, compliant AI systems. 

Inadequately validated models can produce wrong decisions and false flags, creating blind spots in an institution’s monitoring and decision-making process. These gaps can result in missed opportunities or undetected risks, directly impacting operational efficiency and the bottom line.The ability to not only implement AI models but also to demonstrate their effectiveness and explain their decision-making process becomes crucial in this context. 

Vendor transparency is a crucial factor in maintaining the integrity of AI-driven compliance and communications monitoring processes. 

Good vendors distinguish themselves by their willingness to be open about their training methods and the statistical analyses they employ to validate their results. This transparency helps you verify the vendor’s results against your data—a step that is essential in ensuring the model’s applicability to your institution’s context. 

Moreover, this level of transparency facilitates a deeper understanding of the model’s strengths and limitations. The ability to fine-tune models based on a thorough understanding of their inner workings also addresses concerns around regulatory compliance and business risks.  

You can demonstrate to regulators that you not only understand the AI models you’re using but also can adapt them effectively. 

The question is, what should you ask your vendors and what does vendor transparency look like practically? 

The core principles of vendor model validation 

The best communications surveillance vendors do more than build models—they build governance and transparency from the ground up. That includes: 

  • Model explainability 
  • Model monitoring 
  • Good change management  
  • And balanced datasets. 

It’s important that vendors explain how their models were developed, the nature of the datasets they were trained on, and collaborate with you to clarify discrepancies in results. Model explainability is fundamental to transparency. Usually, explainability is a collaborative process between the vendor and the firm, with the vendor sharing statistical models that justify confidence in outputs and firms reviewing assumptions against internal data. 

A good vendor also tunes its models on your data and refines them based on your labels and annotations in output. Closely tied to explainability are model transparency, fairness, and interpretability. 

As regulators and stakeholders increasingly scrutinize AI-driven decision-making processes, vendors must demonstrate that their models are free from bias and discriminatory practices

Aside from ensuring ethical AI development, the following are key aspects of transparency and fairness: 

  • Tool development and use cases that address specific risk types mapped to industry regulations 
  • Internal charters and policies that explicitly commit to ethical AI development practices 
  • Explainability scores and ongoing reporting on AI-driven selections to enable decision-making interrogation 

Model monitoring and change management are the other critical components of responsible AI implementation for compliance and communications monitoring. While modern LLM models generate fewer false positives, rigorous change management processes remain essential. 

Look for vendors that implement changes only with explicit customer consent and thorough testing and validation. Each update should be treated with the same level of scrutiny as an initial implementation, allowing customers to test and verify the changes against their specific needs and internal controls. 

Lastly, pay attention to the kind of data a vendor uses. Synthetic data plays a valuable role in enhancing datasets and improving model performance. Reputable vendors understand that an overreliance on synthetic data can potentially limit the diversity and real-world applicability of their models. 

While synthetic data can be useful for augmenting datasets and providing additional learning examples, it should not completely replace real-world data, especially in the critical phases of testing and validation. 

The ideal approach involves a balanced mix, leveraging synthetic data to enhance model training while prioritizing real customer data for final testing and validation. This ensures that the AI models can effectively handle the nuances and complexities of actual financial communications and transactions. 

Charting a course for transparent GenAI adoption 

The future of finance is intertwined with AI and it’s a future brimming with opportunity for those prepared to embrace it responsibly. A truly valuable partner distinguishes itself through an unwavering commitment to transparency and rigorous model validation methods. 

At Shield, we embody these principles through: 

  • Comprehensive test sets that enhance customer understanding of our models’ operations. 
  • Custom model tuning using client data to ensure relevance and accuracy. 
  • Explainability scores for alert triggers, providing insight into model decisions. 
  • Thorough AI model documentation to support regulatory compliance efforts. 
  • A collaborative change management process that prioritizes customer consent. 
  • Strict adherence to all major AI guidelines including the EU AI Act, FINRA, and FCA regulations. 

Learn more about how Shield can boost your firm’s compliance monitoring processes through the innovative GenAI development of AmplifAI. 

Discover the 5 essential questions to ask when evaluating AI compliance vendors

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