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Model Explainability

When discussing AI for risk modeling, the topic of model explainability inevitably arises. Traditional simulation-based risk models provide transparency because you can examine the underlying assumptions and equations being used. On the other hand, neural networks are often seen as less transparent due to the millions of weights encoding their learning, which obscure an intuitive understanding of their behavior. However, Riskfuel AI risk models are explainable by design. Let’s explore the reasons why:

1. Unbiased Training Data Source: AI models can unintentionally perpetuate or amplify biases present in the historical data they are trained on. Riskfuel’s approach is unbiased since it relies on data generated by the traditional simulation-based model (referred to as the “oracle”) it aims to replicate.

2. Coexistence with Traditional Model: Riskfuel’s model operates alongside the original simulation-based model, which remains accessible. This setup allows for the attribution of any unintuitive results to specific model components and data sources, preserving accountability. By having access to the “oracle,” we gain full insight into the AI model’s risk assessments.

3. High Accuracy: Achieving a high level of accuracy is a key aspect of Riskfuel’s approach to explainability. This ensures that the AI risk model closely aligns with the original model, making it easier to make meaningful comparisons.

Through these strategies, Riskfuel combines the speed of neural network-based risk modeling with the transparency necessary to foster trust, accountability, and regulatory compliance.