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

Validation of artificial intelligence models in the financial sector has been one of the most crucial phases of the artificial intelligence models’ lifecycle. 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 artificial intelligence algorithms. Extension is not only limited to credit risk but can also apply to divergent business domains. This paper highlights the risks of using artificial intelligence in financial applications and provides significant motivations for having an artificial intelligence validation framework to control and eliminate those risks. Besides, we underline the details of our framework’s pillars by mapping them to well-known validation contexts such as conceptual soundness, model performance and model usage.