Exploring Use of GenAI in P&C

 

Tom Kerr (TK): Mainstream use of generative AI has prompted claims leaders to explore how the technology can be used to improve claims management. GenAI无疑为P提供了巨大的机会&C industry, tech leaders must be diligent in ensuring the intricate knowledge and experience in claims management is integrated into these programs.

In today’s podcast, 我们邀请了Enlyte的技术专家Mike Bishop和Mike Cwynar来讨论这些问题. Gentleman, welcome to show.

Mike Bishop, let’s start with you. 当您听到客户将GenAI纳入他们的索赔程序时, what are they typically looking for?

Mike Bishop我认为他们现在所做的就是每个人都在做的, which is trying to understand the technology. So, it’s been hyped, it’s come on the scene very recently, 我认为每个人都看到了这项技术的巨大前景, but they’re trying to understand how to use it.

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, but to actually incorporate it into a workflow, 尤其是涉及到治疗受伤员工的工作流程, people injured in automobile accidents, you have to be very careful about how you use it.

So, 我认为现在客户正在做的是试图理解用例的种类, where it would be applied and also, to understand how they can use the technology responsibly.

TK: Do they come with a general idea of how they’d like to incorporate the technology in their claims management programs, or is there a learning gap that still exists?

Bishop: I think there’s definitely a learning gap. Certainly, they can come with ideas, the latest press release that they’ve read, 或者他们可以想出一些技术上的想法，你可以用它来做, right?

Like, "Oh, I could summarize this, or I could have a chatbot that does that," but nothing specific. And I think, again, 他们只是想更好地理解什么类型的事情是真正可能的, and then again, how they’re going to incorporate them. So, I think there absolutely is a learning gap.

Mike Cwynar: Yeah, I think most come in with a list of problems that they think that they can solve with this just given an understanding of their businesses the potential behind GenAI.

作为商人，我不知道他们总是知道从哪里开始. They just have a problem. And to Mike’s point, some recent announcement sounds like maybe it would check that off their list. 我能快速预测索赔是否需要更长期的东西吗? Today, sometimes they don’t know until later on in that cycle. Tomorrow, they’d like to figure it out sooner rather than later through the use of historical data.

Many just don’t necessarily know where to start and I think that’s where the learning gap comes into play. 因为这并不一定像纸上写的那么简单.

TK: And to continue on that theme, Mike Cwynar, what other challenges do claims industry professionals face in better understanding and implementing this technology?

Cwynar坦率地说，我认为有了数据，人们对此有了更多的了解. 你开始问这样的问题，“你有这个的数据吗? When’s the last time you cleaned any of it up?”

您最终从许多(至少是较大的付款人)那里听到的是多个系统中的数据. It’s not necessarily that easy to get to. 我认为仅仅有大量的数据并不能解决问题. 适当数量的数据对于构建和训练这些模型非常重要.

And I think the last part of it really is who within the organization is the subject matter expert that can help validate that these models are producing answers to the questions the same way that their business typically would? That’s the power behind it. Every payer can have their own sort of unique way of settling claims and handling underwriting, etc. And, so, GenAI的力量是为了让他们在做决定时拥有自己的哲学.

但是训练这些模型需要时间，而且这些模型需要数据. And, not all data is good data. And so, 我认为这是很多人真正开始关注的地方, 意识到有很多实体都有这些潜在的强大模型, but they’re not any good without customer data.

Bishop: I think the other one, too, that is not as well appreciated sometimes is the fact that the technology providers that typically serve our industry and serve all industries, 也在尝试学习这些技术，但他们真的还没有准备好.

Whenever you meet with the technology providers, they’re still trying to figure out how they’re going to make some of these core technologies available to us. 所以，它真的还没有确定在哪里你可以做出技术上的选择.

Most people cannot rationalize going out, for example, and training a large language model on their own. They’re going to build off of one, specialize it through various techniques, including prompt engineering.

但因为技术供应商现在还没有准备好, you’re almost in a wait‑and‑see mode where you’re trying to figure out who’s going to come to market with the best tools. 所以，在那之前，我认为这是把这些东西推向市场的另一个障碍.

TK: And, I think that's a good transition to our next topic. 付费用户在选择GenAI技术合作伙伴时应该问哪些问题?

Bishop: First of all, you want to make sure that the tech provider appreciates the particular challenges in our industry. One of the euphemisms that they have around these large language models is the errors that can come up. They call them hallucinations or various terms, 这就意味着模型给出了错误的结果, that’s made up. And so, you have to deal with all those things.

If that happens when you’re trying to figure out something that you’re buying online or something where the ramifications are small, it’s not as big of a deal. If you’re providing health care information to someone, the ramifications of that could literally be life‑threatening. And so, 你必须确保提供模型的人, that are providing the technology, 是否考虑到你将如何负责任地使用这项技术. And to make sure that the guardrails are there.

Things like biases can creep into these models. 只是要确保他们不只是在使用技术，或者以那种方式看待它. 他们正在考虑如何将其应用到我们的行业中.

TK: 玩家可以遵循哪些策略来确保他们从GenAI中获得最佳结果?

Cwynar: I think one of the things that we’ve been talking to many about is having a very clear understanding of the problem statement and how you measure that, you’re getting the outcome that you want.

所以，就像迈克刚才说的，监控不合理答案的能力. For example, there’s a model that’s running that’s helping an adjuster make decisions that have some regulatory compliance background in them, and all of a sudden, a new fee schedule in Florida comes out or Michigan comes out with some of these models that could potentially become irrelevant overnight.

So, I think one of the biggest things is to really make sure that, in all cases where you’re running this, that some amount of subject matter expertise, human in the loop, is involved to, if nothing else, periodically audit and look at the results that are coming out of the models to make sure that they continue to be relevant.

Because these aren’t one‑and‑done things. 我们不做一次，然后它们就会一直工作下去. 你必须定期对这些东西进行管理，这样才能形成良好的数据卫生，对吧?

Same thing with data. It’s going to change over time, and so you got to just be really on top of that kind of governance model to make sure that these models don’t start providing answers that potentially become irrelevant at some point in the future and no one really understands it.

TK: So, 就目前GenAI在理赔管理领域的工作情况进行评分而言, are payers mostly looking at how well it can make processes easier or make claims handling more efficient? Or are there are other goals they want to achieve?

Cwynar Yeah, it’s like progress over perfection. 你没有必要一开始就处理最复杂的问题. 从小事做起，适应它，了解如何监控、控制和审计它.

Because the possibilities of what you can apply this to become somewhat unlimited within the world of claims, when you think about interacting with policyholders, injured employees, etc.从它对承保的潜在影响到欺诈检测，都在里面. But, it’s reliant on people that really understand the business and the ability to keep an eye on what’s going on day to day.

TK: 你对GenAI在未来一到五年内将如何影响这个行业有什么预测吗?

Bishop: I think you’ll see it in particular areas. And I think, 我和迈克给出的几乎所有答案中都有这个线索, 你是否必须考虑治理以及如何负责任地使用技术.

And so for that reason, 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, in our case, an injured worker directly interacting with the technology. I think the technology will have a bigger impact behind the scenes because then you can control it.

So I don’t see it happening in the next one or two years where we would provide a medical summary to a claimant, but we could certainly provide a medical summary that came out of GenAI to someone internal to make sure that they would look out for these errors, hallucinations, and those kinds of things.

So, it’s the responsible use requirement that I think, especially in our industry, is going to mean that the technology will be used behind the scenes for automation to sort of help knowledge workers do their job more efficiently and better.

Cwynar: Yeah, I agree. 我认为速度和效率是这里最大的机会. People are still going to file claims and they’re going to need help from time to time and you’re going to always want a human being available to have a conversation.

So, I don’t see this world where all of a sudden there’s a virtual adjuster who handles everything for you. That doesn’t mean that, to Mike’s point, behind the scenes, there isn’t a virtual adjuster that’s able to quickly and efficiently gather all the information that’s needed, provide recommendations, help sort further claims along much faster than they can today given the typical amount of work that sits in front of an adjuster.

我想你会在那里看到更多的助理理算员, 虚拟调节器组件是相当强大的许多我们今天的行业. And, I think the ability to be able to quickly triage, 并获取信息，否则不会总是可用的. 比如说，如果有人刚刚提出索赔，他们之前发生过事故, which would alter the course of the care that somebody needs to be able to identify things like that upfront.

它只是在早期快速地创造了更多个性化的护理计划, which then gets people better and off to their lives faster. 这些可能是许多人正在谈论和关注的一些领域, 但我认为在未来几年内最有潜力.

TK: Thanks, Mike and Mike. And we’ll be back with another podcast soon. Until then, thanks for listening.