In a recent episode of Insurance News Analysis, I had the honor of sitting in the hot seat—which is typically occupied by Kenneth Saldanha himself—with Bruce Hentschel, a seasoned professional in the field, to discuss what responsible usage of generative AI in insurance actually entails.
It is about much more than efficiency and automation, spoiler alert. It has to do with morality, compassion, and keeping people well informed.
An Important Warning: The Unticked Box
We began with a tale about a 76-year-old man in the UK who had his home insurance claim rejected following a fire, which goes right to the core of the problems with the conventional insurance paradigm. Why? Years ago, he did not check a box on the application form.
Although it is simple to dismiss this as a terrible exception, it is actually a clear warning. People are frequently not met where they are by insurance procedures. We are asking people—who are frequently weak, overburdened, or simply human—to understand high-stakes, legalistic questions with the accuracy of an attorney.
Generative artificial intelligence can assist in resolving this type of conflict.
Humanizing, Not Just Automating, with Gen AI
Generative AI can go through data, validate it, and provide clear, accurate summaries for the customer to affirm, instead of relying on them to remember and self-report prior claims or risk factors.
The goal is to handle the heavy lifting so that the human may concentrate on making an informed and self-assured choice, not to exclude them. Imagine an insurance application that is less like completing a tax return and more like a discussion.
When used properly, generative AI can transform the transactional consumer experience into an intuitive one. And that is revolutionary in a field where trust is crucial.
The problem is that AI can also make mistakes.
Generative AI is not the solution. Without adequate guidance, it can produce hauntingly plausible nonsense, reinforce biases, even produce hallucinatory facts. Because of this, responsible implementation is the full concept, not just a catchphrase.
We investigated the rising need for insurance plans that genuinely compensate for generative AI errors. Imagine a policy that activates when your AI prepares a loan approval letter automatically based on erroneous data, misinterprets a medical record, or creates defamatory content.
Similar to cyber insurance, this market will probably begin small, covering certain, well-defined risks, and grow as rules and case law develop.
However, the message is unmistakable: AI is strong, and strength necessitates limitations.
Conscientious AI: A Prospective Structure
What does ethical AI in insurance look like? We think it begins with a few fundamental ideas:
People remain informed. Instead of replacing human judgment, AI should enhance it.
openness as opposed to black-box wizardry. Both underwriters and customers must comprehend the model’s actions and motivations.
Bias needs to be recognized and dealt with. We must demonstrate that our models are equitable; we cannot simply hope for it.
Use cases need to be carefully defined. Particularly in regulated industries like insurance, just because AI is capable of doing something does not imply it should.
5 .The Ideal Combination of Innovation and Risk Management
Finding the balance between risk and return is essential to the insurance industry’s success. This also applies to generative AI. Insurers who embrace this change with integrity, skepticism, and inquiry will not only survive it, but will take the lead.
Faster underwriting and intelligent chatbots are not the only things that will shape the future of insurance. The goal is to create a system that is more intelligent, secure, and compassionate for all those who rely on it. Yes, the 76-year-old who failed to tick a box is included in that.
Because people are at the heart of insurance. And generative AI ought to be developed with that user in mind.
