I am dreading the inevitable future day when I have to argue about a denied health insurance claim on the phone with a fucking robot prompted to make it as difficult as possible for me to win.
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Optimistically, it might be an improvement. Imagine a world where you can call in or chat. Hold time is zero in both cases. You never need to "transfer" to a new department, or supply the same information over and over. LLMs have access to all the case files and needed info on the backend instantaneously.
Health insurance companies may still seek to deny claims, but the fog of bureaucracy is no longer so easy to hide behind. LLMs should be able to give you a plain-english explanation of why your claim was denied instantly. If they refuse, there will inevitably be consumer-advocate LLMs that can cite the specifics of your health insurance plan in response, or argue on your behalf. I think companies will find that this is a big waste of time, and cut to the chase. Either there is a legitimate reason for claim denial or not. If there is legitimate disagreement on the interpretation of your plan, escalation to lawyers and human review can be fast-tracked.
Often, holding times are not only due to a shortage of call center employees available, but also to encourage the caller to give up. I doubt the hold time would be zero, unless the government legislates a maximum hold time. Even then, the AI could stall with endless patience and requesting clarifications and further information, and repeating details back to you to confirm, with minor errors requiring correction, which might be even worse than waiting on hold.
Do you have direct experience with a call center where this was happening? This sounds like a ridiculous conspiracy theory. In every call center I've ever had experience in, median/mean hold time was a KPI that was relentlessly optimized for. Hourly demand was modeled and staffing levels were set such that hold times were kept to the minimum possible with the resources available. Calls-not-answered was an inverse metric. That's not to say the optimal hold time is always zero. It's also bad to have too many staff sitting idle "just in case" demand suddenly peaks. It's a tricky problem. But LLMs obviously obey the laws of compute scaling, not human scaling, so I would expect it to scale to demand very quickly.
Hold times are optimized for customer retention and profits. This implies that you’re both correct. It’s just the perspective: if people give up on their query but stay a customer that’s fine. Most call centers don’t exactly have the degree of customer service of a hospitality institution like a luxury hotel.
I’ve always appreciated the perspective that they just figure out how much staff they need to stop the customers from getting too pissed off.
The startup opportunity is to have an adversarial chatbot argue on your behalf. Feed it the terms and conditions of your policy, claim info, and then have it duke it out with the insurance company.
"better" for whom? The key issue raised by the parent post is that from whatever sources the robot learns, we should expect it to be trained to use that data to make things better at achieving the goals of the robot owner which are different from - and often opposite to - the goals of the customer.
So yes, in comparison to a static robot flow or a human following a script, a robot that learns from past calls would be better... at maximizing the chances that the caller gives up in frustration while still having their insurance claim denied.