Contextual Guidance for Autonomous Decision Making in Insurance Video Transcription
AI systems cannot be black boxes when making decisions in insurance. When setting prices, denying insurance or denying a claim payment, you must be able to explain this to customers.
Why is Explainability Important?
But it’s also beyond customer service. In some cases, there may be legal implications and you need to justify why a decision was made in a court of law. Explainability is also important to build trust in the companies and employees using the AI systems. Until there is trust and explainability, AI systems will not be deployed.
What Type of Technology is Used to Maximize Explainability
Explainability is dependent on the type of mathematics and technology that you use to help make those decisions. At Daisy, our reinforcement learning and fuzzy logic system provides contextual guidance and explainability and rationale for all elements of the decision. At Daisy, we see what others don’t.