Why Insurers Must Prioritize Explainable AI Now 

The insurance industry is booming with the rise of artificial intelligence. From increased efficiency and innovation, AI has transformed the world. However, it has also raised concerns about transparency. Explainable AI (XAI) is becoming a crucial requirement, with regulators and consumers demanding clearer insights into AI-driven decisions.

What is Explainable AI? 

Explainable AI (XAI) is an AI system that provides clear and understandable reasoning behind its decisions. In the insurance world, this means that if an AI model denies a claim, it should also be able to explain why, whether it’s due to specific data inputs, predefined rules, or algorithmic weighting. 

Explainable AI vs. Traditional AI 

It’s important to note that Explainable AI is not a separate form of AI – rather, it is a feature that makes AI’s decision-making transparent.  A traditional AI usually shows you just the results, without showing how it got there. XAI, on the other hand, allows insurers to:

• See which data inputs influenced the decision.  
• Clearly understand how rules, weights, or algorithms contributed to the outcome.  
• Provide a clear, human-readable explanation of AI-generated results.

Lets put it this way, if AI denies a claim, XAI can explain the reason behind that, whether it was due to missing documents, policy terms, or fraud detection. 

Why Insurers Can’t Ignore XAI 

Many insurers have adopted XAI, but not all are focusing on making it transparent. Even though regulators are increasingly focused on ensuring that AI models are ethical, unbiased, and transparent, there can still be trouble for you. Consumers want to understand how an AI-driven decision can affect their claims, policies, and pricing, and if you cannot explain that effectively, it might result in reputational damage, regulatory penalties, and customer distrust, as emphasized by Mike Fitzgerald, an insurance technology expert. 

At this point, both customers and regulators are now demanding clearer AI decisions, with all the hows, why, and whens. Regulators want visibility into AI training data, model structure, and decision-making logic to ensure fairness, and consumers prefer AI solutions that they can easily understand, and this is something that will increase their trust in insurers as well.  

How insurers can stay ahead 

To keep up with the growth in demand by consumers and regulators, insurers can: 

• Use AI systems that have built-in explainability and can allow clear reporting on how decisions are made.  
• Develop proper documentation and reporting processes. This doesn’t necessarily mean you document every decision manually, but rather, you must have the ability to track and explain outcomes when needed. 
• Train your claims adjusters, underwriters and customer service staff members to be able to interpret AI decisions effectively, to be able to assist clients. 
• Ensure compliance with all regulations; therefore, staying up to date with any changes in regulations would be a plus point. 

Explainable AI builds trust with customers and ensures insurers maintain control over their AI systems. If insurers begin to focus on transparency, they may be able to create a better customer experience with a 99% satisfaction rate.  

AI is constantly revolutionizing. While it is an excellent tool, making much of our jobs easier, but without Explainability, AI could rather become a liability than an asset. Now is the time to act!