Increasing trust to artificial intelligence in finance: AI model validation framework

Increasing trust to artificial intelligence in finance: AI model validation framework

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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Data to anchor a new age of risk management

Today, modern enterprises must tackle unstructured data, semi-structured data and data with high variety, velocity and volume. But current data systems for compliance cannot perform the requisite advanced analytics that require scale.

Data to anchor a new age of risk management

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