Tom Kerr (TK)生成式人工智能的主流使用促使理赔负责人探索如何使用该技术来改善理赔管理. 和 while GenAI certainly offers tremendous opportunities for the P&C行业, 技术领导者必须勤奋地确保将索赔管理方面的复杂知识和经验整合到这些程序中.
在今天的播客中, we invited Enlyte tech experts 迈克主教 and 迈克Cwynar to discuss these matters. 先生们，欢迎光临.
迈克·毕夏普，从你开始吧. When you hear from customers regarding incorporating GenAI into their claims programs, 他们通常在寻找什么?
迈克主教: I think 正确的 now what they’re doing is what everyone is doing, 这是在试图理解这项技术. So, 它被炒作了, 它是最近才出现的, and I think everyone sees a great deal of promise in the technology, 但他们正在努力了解如何使用它.
这是所有这些技术最困难的部分? 为技术而研究技术是一回事, 而是将其整合到工作流程中, especially a workflow that is involved in treating 受伤的员工, 在汽车事故中受伤的人, 你必须非常小心如何使用它.
So, I think 正确的 now what customers are doing is trying to understand the kinds of use cases, 它会应用在哪里, 了解他们如何负责任地使用这项技术.
TK: 他们是否对如何将这项技术纳入他们的索赔管理计划有一个大致的想法, 还是仍然存在学习差距?
主教我认为肯定存在学习差距. 当然, 他们可以有自己的想法, 他们读到的最新的新闻稿, or they can come up with ideas of technical things that you could do with it, 正确的?
Like, "Oh, I could summarize this, or I could have a chatbot that does that," but nothing specific. 我认为, 再一次。, they’re just trying to understand better what types of things are truly possible, 然后再一次, 他们将如何整合它们. 所以，我认为绝对存在学习差距.
迈克Cwynar:是啊, 我想大多数人都有一系列的问题，他们认为他们可以用这个来解决，只要了解他们的业务，GenAI背后的潜力.
As businesspeople, I don’t know that they always know where to start. 他们只是有个问题. 和 迈克的观点, some recent announcement sounds like maybe it would check that off their list. Can I predict quickly whether a claim’s going to need something longer term or not? 今天，有时他们直到这个周期的后期才知道. Tomorrow, they’d like to figure it out sooner rather than later through the use of historical data.
许多人只是不知道从哪里开始，我认为这就是学习差距发挥作用的地方. Because it isn’t necessarily as easy as it sounds on paper to figure something like that out.
TK: 继续这个主题, 迈克Cwynar, 在更好地理解和实施这项技术方面，索赔行业专业人士还面临哪些挑战?
Cwynar: I think with data, quite frankly, folks are getting a little more educated about it. You start asking questions like, “do you have the data for this? 你上次打扫是什么时候?”
What you end up hearing from a lot of, at least the larger payers, is data in multiple systems. 它不一定那么容易到达. 我认为 just having a lot of data doesn’t necessarily even solve the problem. The 正确的 amount of data is going to be important for building and training some of these models.
我认为最后一部分是组织中谁是主题专家，可以帮助验证这些模型是否以与他们的业务通常相同的方式产生问题的答案? 这就是它背后的力量. 每个付款人都可以有自己独特的方式来解决索赔和处理承保等问题. 和, so, the power of GenAI is designed to let them have their own philosophy built into these decisions.
但 it takes time to train these models, and these models need data. 并不是所有的数据都是好的数据. 所以, I think that’s where a lot of folks are really starting to put more focus in here, realizing there’s a lot of entities out there coming with these potentially powerful models, 但如果没有客户数据，它们就毫无用处.
主教我想是另一个, 太, 有时候，我们并没有很好地认识到，技术提供商通常为我们的行业服务，为所有行业服务, are also trying to learn these technologies and they’re really not ready.
当你和技术供应商见面的时候, 他们仍在努力弄清楚如何将这些核心技术提供给我们. So, it really hasn’t settled down where you can sort of make a technical choice.
大多数人无法为外出找理由, 例如, 并且自己训练一个大的语言模型. 他们将以一个为基础, 通过不同的技术使之专业化, 包括即时工程.
但 because the technology providers really aren’t ready 正确的 now, 你几乎处于一种观望模式，你试图弄清楚谁将带着最好的工具进入市场. 所以, until that plays out, I think that’s another obstacle to bringing this stuff to market.
TK: 我认为这是我们下一个话题的一个很好的过渡. What questions should payers ask when they're selecting a GenAI tech partner?
主教首先, 你要确保技术供应商理解我们行业的特殊挑战. 他们在这些大型语言模型中使用的委婉语之一就是可能出现的错误. 他们称其为幻觉或其他术语, which really just means the model is spitting out something that’s just wrong, 这是编出来的. 所以，你必须处理所有这些事情.
如果这种情况发生在你试图弄清楚你在网上购买的东西或影响较小的东西时, 这没什么大不了的. 如果你向某人提供医疗保健信息, 这种后果可能会危及生命. 所以, you have to make sure that the people that are providing the models, 他们提供了技术, are taking into consideration how you’re going to responsibly use the technology. 并确保护栏在那里.
偏见之类的东西会潜入这些模型. Just making sure that they’re not just using technology and sort of viewing it that way. 他们正在考虑如何将其应用到我们的行业中.
TK: What are some strategies payers can follow to ensure they're getting the best results from GenAI?
Cwynar我认为我们一直在和许多人谈论的一件事是对问题陈述以及如何衡量它有一个非常清晰的理解, 你得到了你想要的结果.
So, like Mike was just talking about, the ability to monitor for answers that don’t make sense. 例如, 有一个正在运行的模型可以帮助调整员做出有一些法规遵从背景的决定, 突然之间, 佛罗里达州或密歇根州出台了新的收费计划，其中一些模式可能会在一夜之间变得无关紧要.
So, 我认为最重要的事情之一就是要确保, 在你运行这个的所有情况下, 这需要一定的专业知识, 圈内人, 与…有关, 不出意外的话, 定期审计和查看从模型中得出的结果，以确保它们继续具有相关性.
因为这些不是一劳永逸的事情. We don’t build it once, and then they just kind of work in perpetuity. You have to be regularly on top of this stuff, which then gets into good data hygiene, 正确的?
数据也是一样. 它会随着时间而改变, 因此，你必须真正掌握这种治理模型，以确保这些模型不会开始提供答案，这些答案可能在未来的某个时候变得无关紧要，没有人真正理解它.
TK: So, in terms of score-carding how well GenAI works in the claims management space 正确的 now, 支付者主要关注的是它能在多大程度上简化流程或提高索赔处理效率吗? 或者他们还有其他想要实现的目标?
Cwynar 是啊，就像进步胜过完美. You don’t necessarily need to tackle the most complicated problem 正确的 out of the gate. Start small, get comfortable with it, understand how to monitor, control and audit it.
因为在索赔的世界里，你可以应用它的可能性是无限的, 当你考虑与投保人互动时, 受伤的员工, 等., to potentially how it influences underwriting to fraud detection, it’s in there. 但, 它依赖于真正了解业务的人，以及关注每天发生的事情的能力.
TK: Do you have any predictions on how GenAI will impact the industry in the next one to five years?
主教我想你会在一些特定的领域看到它. 我认为, this has been a thread that has been in almost all of the answers that Mike and I have given, is that you have to think about the governance and how you responsibly use the technology.
因此，出于这个原因, I think the impact that you’re going to see is less the stuff that is hyped and demoed; where you see an individual customer or, 在我们的例子中, 一个受伤的工人直接与技术互动. I think the technology will have a bigger impact 幕后故事 because then you can control it.
所以我认为在未来一两年内我们不会向索赔人提供医疗摘要, 但我们当然可以提供一份来自GenAI的医学总结给内部人员，以确保他们会注意这些错误, 幻觉, 诸如此类的事情.
So, 我认为这是负责任的使用要求, 尤其是在我们这个行业, 这是否意味着这项技术将在幕后用于自动化，以帮助知识工作者更有效、更好地完成他们的工作.
Cwynar是的，我同意. I think speed and efficiency become the biggest opportunities here. 人们仍然会提出索赔，他们会时不时地需要帮助，你总是希望有一个人可以和你交谈.
So, 我不认为这个世界会突然出现一个虚拟调解员来帮你处理一切事情. 这并不意味着, 迈克的观点, 幕后故事, 没有一个虚拟调节器能够快速有效地收集所需的所有信息, 提供建议, 帮助排序进一步索赔比他们今天可以更快地考虑到典型的工作量，坐在前面的理算员.
我想你会在那里看到更多的助理理算员, the virtual adjuster component being pretty powerful for a lot of our industry today. 和, 我认为是能够快速分诊的能力, and access information that otherwise wouldn’t always be available. Say if somebody just filed a claim and they’ve had prior accidents, 这将改变护理的过程，人们需要能够提前识别诸如此类的事情.
It just creates more of a personalized plan of care very quickly and early on, 然后让人们更好，更快地开始他们的生活. Those are probably some of the areas that many are talking about and focused on, but I think have the biggest potential over the next couple years.
TK: 谢谢，迈克和迈克. 我们很快会在另一期播客中回来. 到那时候，谢谢收听.