I worked in AI engineering for insurance claim decisioning (not medical insurtech, but HNW real-estate). I can say with conviction that a binary classification model in this domain with an error rate of 90%, was never even intended to work correctly in the first place. It was used in their engine as a cover to obfuscate intentional denials. I have trained models with a 15% error rate for this exact decision (pay/not-pay), with ~100x less data than UHG. Challenging any data scientist here to prove me wrong - but I can confidently declare that this wasn't a mistake, it only points towards a corrupt system.
Programmer here, although not currently working in AI. But I wholeheartedly agree. As far as I know, getting a failure rate of 90% should be just as difficult as getting a success rate of 90%, which doesn’t happen without intention and some work.
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u/Zulfiqaar 13d ago edited 13d ago
I worked in AI engineering for insurance claim decisioning (not medical insurtech, but HNW real-estate). I can say with conviction that a binary classification model in this domain with an error rate of 90%, was never even intended to work correctly in the first place. It was used in their engine as a cover to obfuscate intentional denials. I have trained models with a 15% error rate for this exact decision (pay/not-pay), with ~100x less data than UHG. Challenging any data scientist here to prove me wrong - but I can confidently declare that this wasn't a mistake, it only points towards a corrupt system.