Validating artificial intelligence (AI) models in the financial sector is one of the most crucial phases of AI models’ lifecycles. Although the industry is highly regulated and already familiar with validating traditional statistical methods in credit risk, these need an extension and adaptation to their as-is validation standards and frameworks for advanced AI algorithms. Extension is not limited to credit risk but can also apply to divergent business domains. This paper highlights the risks of using AI in financial applications and provides significant motivations for having an AI validation framework to control and eliminate those risks. It also underlines the details of Prometeia framework’s pillars by mapping them to well-known validation contexts such as conceptual soundness, model performance and model usage.
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