Grow Insurers’ Trust in AI-Powered Decisions with Explainable AI
As we enter 2022, the pace of change in the insurance industry has become unlike any other time in history. Insurers have recognized the need for AI technology to optimize core processes and minimize pandemic related challenges. As a result, AI investment will increase significantly over the next couple of years.
Though there is no doubt that AI technologies are propelling companies to success, many organizations still struggle to successfully implement AI due to a lack of trust and understanding in the technology itself. However, when insurers leverage AI systems that deliver explainable decisions (more on explainable decisions below), they receive clear insight into the AI’s decision-making logic – building trust in its recommendations and making the adoption of the technology far easier to accept.
Trust is Not Built Blindly: How to Mitigate Mistrust in AI Systems
According to an article from Towards Data Science, two leading reasons for mistrust in AI technology are:
- the AI technology is a black box
- those within an organization do not have the expertise on hand to demystify AI technology
What is a Black Box?
A black box is a system that provides visibility to its inputs and outputs but provides no insight on the processes in between. Trust cannot be developed blindly - a lack of visibility into a system’s internal processes means its outputs will be difficult to accept.
Demystifying AI: A Focus on Transparency
Though in many instances users of AI systems are allowed a look into its inner processes, many organizations do not have the expertise necessary to gain a complete understanding of the technology itself – resulting in continued distrust.
However, when the focus is placed on transparency in decision-making, trust between insurers and AI technology grows. Leveraging AI systems that deliver explainable decisions builds trust by providing a clear rationale for why the AI is making the decisions it is.
Explainable Decisions in the Insurance Space
In insurance, this may look like an explanation for why a claim was denied due to fraud. Suppose a claim for windshield damage caused by a storm was deemed fraudulent by an AI system. With no further explanation, it may seem incorrect that this claim was flagged as fraud – as it is very plausible that a storm could result in such damage.
However, when the logic behind the AI’s decision is revealed – i.e., the claim was denied because the repair costs far exceeded the typical costs for similar vehicles with similar windshield damage – it becomes clear that the decision was justified, making insurers confident in the AI’s recommendations.
The Organizational Value of Explainable Decisions
Furthermore, building trust in AI technology with explainable decisions has the power to elevate the role of insurers in the workplace. For example, when insurers trust AI to take on the mundane tasks it is far better suited to do, they are freed to take on the high value tasks they do best – business strategy and effectively meeting genuine customers’ needs.
AI has permeated nearly every industry and has quickly become a cornerstone in today’s insurance space. Though implementing AI can be a challenging initiative, leveraging AI systems that deliver explainable decisions make the process far easier - quickly building trust and freeing insurers to focus on their continued success in this transformative year of business.