Financial Experts Warn AI Could Make You ‘Uninsurable’ — How To Avoid This With Your Insurer
Written by
Kristopher Kane
Edited by
Amber Barkley
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Artificial intelligence is changing everything, from the way we work to how we bank and, now, how we go about securing insurance policies. As AI technology continues to reshape industries, the insurance sector must navigate its integration carefully to prevent the possibility of AI rendering some consumers uninsurable — for unknown reasons.
Keep reading for a look at some of the latest concerns about AI, how it may be used by the insurance industry, its serious potential downsides and what you can do to protect yourself.
AI in the Insurance Industry: Why It’s a Problem
AI has penetrated various sectors, from entertainment to cybersecurity, finance and more. Its use has promised innovation and efficiency unmatched by human efforts, but it hasn’t been without controversy or concern. In the insurance industry, AI is transforming processes like risk modeling, customer service automation and fraud detection.
Its adoption in that first task — risk modeling — has some experts speaking out. While AI-driven personalization has the potential to revolutionize how insurance companies assess consumers and determine policy prices based on modeled risk profiles, it brings with it the troubling possibility that it could inadvertently make some consumers uninsurable, The Daily Upside reported.
Worse still, there may be no clear or decipherable reason for the denial of coverage that some people may face. The problem lies in AI algorithms operating in an opaque, “black box” manner that their human operators can’t interact with or interpret.
The Black Box Dilemma and Bias in AI
The AI black box problem refers to algorithms making decisions that are not easily understood by the people relying on this kind of automated guidance.
This can mean insured individuals — and even insurance providers — won’t fully understand the rationale behind the AI’s decisions about a person’s insurability or how much their monthly premiums will be. It’s easy to see how this opacity could lead to a lack of accountability and potential discrimination.
Bias is another significant issue and a cause for deep concern both within the industry and the consumer sector. The data fed into AI systems for training is often imperfect and can contain hidden biases that may not even be apparent to human operators. If this tainted data is used by AI in its decision-making process, the outcome could be unjust discrimination.
Ensuring Transparency and Accountability in AI Use
The central challenge for insurance companies in AI integration may be in proving the decisions they reach are both ethical and transparent. Industry experts say insurers should have to prove that their AI-driven decisions are understandable and based on sound rationale, which may not even be possible with the way the technology is applied today.
Industry experts say insurance companies should be called on to rigorously test and continuously monitor their AI systems for bias or unfair practices. They’re calling for regular audits and transparent reporting on AI decision-making processes to help mitigate some of the risks associated with the black box problem.
What Consumers Can Do To Protect Themselves
This is a new problem, and it’s unclear what you can do to make sure you don’t run afoul of AI decisions that could make it impossible for you to receive insurance coverage. While the ultimate responsibility lies with insurance providers to integrate AI technologies responsibly, consumers will likely have to play a role in advocating for themselves and safeguarding their insurability.
There are some steps you can take, however. Educate yourself about how your insurer uses AI and data analysis, and understand your rights regarding transparency and fairness. Express your concerns to your insurance provider and ask about the AI systems it employs. Inquire about any measures it’s taking to preserve transparency and fairness and what procedures it has to address potential biases.
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