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Case Study in Insurance Claims

FM Global Case Study: Insurance Claims Processing

Watch Jennifer Faria, Digital Enablement & Automation Services, AVP of FM Global, explain how they optimized their insurance claims process with Skan's Process Intelligence Platform.


Video Transcript:

The case, we're going to talk about today is around claims processing. We are a large insurance company. So obviously claims are key to making sure that our customers are happy. One of the challenges we continue to have is our book of business grows, which is a great thing. Unfortunately, that usually means that our number of claims that have to be processed is growing as well. Looking at how do we continue to account for that additional growth in work without continuing to add to the size of the teams?

Looking at how do we process claims in a way that is effective and efficient in making sure we retain that integrity, of making those great decisions, having great turnaround time so that our customers are delighted with us and in that customer satisfaction. Finding that balance between doing things quickly, but also doing things well with a very high level of quality.

As we think about some of the business goals of this use case and others that are similar, we want to think about how do we understand what the process is, right? What what really exists, what is happening or not happening? How do we optimize that process? How do we understand what can be better, where there are roadblocks, where there are challenges? And then once we have that understanding, looking at how do we use our tools, our automation and process redesign and digitization tools to make sure that we can then transform that process.

One of the things you don't ever want to do is to automate a bad process, right? You you're just going to make bad things happen faster and that's not what anyone wants. It's really critical to understand the true process and find those efficiencies before you go ahead and take on that next step. You would think that getting that information would be easy, but it's not. And in a lot of cases, it becomes a real challenge to be able to make sure that you've got the right information, that accurate information to be able to make those decisions.

The initial approach is typically manual and there are a lot of challenges with manual processing. First of all, you're pulling employees away from the work that you're doing. So instead of letting them be able to process, you're taking time away from them and you're saying, OK, how do you do this? How can you help me? What does this look like? You're also looking at that one moment in time. And so you're just seeing whatever they're processing at that moment in time, which could be a really simple case. It could be something very complex. You just don't know.

The other thing that is really critical is what are the downfalls of this manual discovery is when employees are being watched, you're going to show you what they think you want to see. They're going to show you like, Okay, here's the way it says, I should be doing this. And so you're getting a very artificial read on the process. You're not understanding what the real life day to day process looks like and how they're actually performing. You're seeing what they want you to see.

That puts you in a very difficult position because if you're going to make decisions around that somewhat artificial view, you're going to miss out on a lot of critical things. One of the learnings that we've had is really around this concept of exceptions. When you talk with someone who's been processing claims or processing invoices or whatever it might be, and you talk to them about, well, what are your exceptions? They don't think of those as being exceptions. These are things that they do every day. But when you actually start to look at the process and you see that client A takes this path and client B takes that path and clients C take a different path. Then you start to understand like, oh, wait a minute here. These are from a process improvement perspective, what we would consider exceptions. So again, manual discovery gets you part of the way, but it is certainly not where you want to be if you want to have some optimal impact.

As we started to work with Skan, obviously there there are other products on the market that we looked at. One of the things that really stood out to us is the approach and the level of detail and the richness of the data that we're able to get in. We are getting down to very task specific levels in understanding not only where employees are going to do their work, but how long they're there and how long things are taking in finding insights like why is someone going to Excel the business owner said, Excel is not part of the process, right?

All of these things that we're discovering as we go through are just helping to be able to give us that data to really improve what the process looks like. And one of the things that you mentioned, Eric, was about compliance, right? Our our information security team looked at the Skan product and they were actually excited, which doesn't happen very often. They're great. They're awesome. I love them. But they were incredibly excited about the way that the Skan tool works because the none of the data is leaving our environment. All of these screenshots that were getting all of this information that we're getting from our work with Skan is staying internal to our network. That gives us a lot of power. And it gives us the ability to understand what's happening. We can still get all of the benefits of the analysis that Skan brings to us, but it keeps all of our data in-house.

The other thing that was really important to us as a global organization is to make sure that we could protect the anonymity of our employees. And so we want to be able to say we've got 10 people performing this process and they're doing it 15 different ways, and let's try to find some consistency and narrow that down. But we never want to have a situation where a manager could look at this data and say, oh, hey, Eric, you were doing this today and you should have been doing that, right? That is not the intention of what this tool brings to us.

A lot of those things are built in. I think what is really the beauty of it, as you mentioned, is that Skan is running in the background. Once we go through the process of identifying the applications that are part of this process that we're looking at, we whitelist those applications. We're telling Skan, we only want you to capture what is related to this particular process. We're able to really narrow that focus, and then it's running in the background. We're not taking employees away from their work or getting an authentic view of what's really happening, how long it truly takes to process something, the variations and the most frequently visited path so we can find out if certain parts of the country are doing things one way or another. There's all this richness that you could just never, ever get from interviews or observations or all of those things.

When you take the security of the product and you combine it with that really powerful data and the analytics that go along with it, it's just a no brainer. Part of what we're talking about organizationally is not just using Skan related to our automation efforts, but even just from a pure process improvement because again, we have the same challenge of can't really make the process better until you understand truly what's happening. So we are very excited about the work that we are partnering with Skan on right now and certainly look forward to many more successful use cases.

Read more about how FM Global Optimizes Insurance Claims with Skan's Process Intelligence Platform