A financial services company had a problem. It faced increased risk exposure from its artificial intelligence (AI) due to inconsistent monitoring, risk identification, governance, and documentation of multiple applications across its business units.
It had to be addressed. The issues potentially exposed the company to poor customer experiences; negative brand image; and legal, regulatory, and compliance violations.
How was this happening? Their AI models and applications were generating results quickly, sometimes within a few hours. And by their nature, AI models have an inherent ability to learn and make algorithmic adjustments to optimize their performance.
The organization’s executives realized that they didn’t have a robust mechanism to manage the risks and ensure the AI algorithms operated within the guardrails of how the company intended them to operate. Further, information on vendor AI models was limited, constraining the ability to identify risks.
The company wanted help managing existing AI risks and to develop a rigorous process for keeping a watch on emerging ones. But to do that and perform risk assessments quickly, the company had to expand its data science, statistical, and risk management capabilities.