‘Most businesses believe that machine learning models are opaque and non-intuitive and no information is provided regarding their decision-making and predictions,’ — Swathi Young, host at Women in AI.
Explainable AI is evolving to give meaning to artificial intelligence and machine learning in insurance. The XAI (explainable AI) model has the key factors, which are explained in the passed and not passed cases.
The features that are extracted from the insurance customer profile and the accident image are highlighted in the XAI model. The rules and logic for claim processing are presented in the output.
In every case, passed cases are explained by showing the passed rules or coverage rules associated with the claim. Similarly, in the case of failed cases, the failed rules are displayed by the XAI model.
In many enterprises in the insurance vertical, the underwriting engine or policy rules engine is a black box. The recommendations, quotes, insights, and claim rejections/passes are generated by the black box without any explanation.