Learn a practical approach to identifying RPA and AI use cases for property and casualty (P&C) insurance core processes.
Sometimes the right tool for a job is the tool you have, at least according to my father. That’s what he always said when I would complain that it was hard to rely solely on a pair of pliers when we inevitably needed to fix something on our family farm in rural Nebraska.
While I understand the sentiment that there is a tool for every job, not everyone can afford to buy a tool every time something comes up. The best tool to have is one you can use for many jobs. The more versatile a tool is, the more useful it is to the user. That goes for any tool, not simply hand tools for home repairs.
Being raised on a farm infused some practicality in me. We often had to get things done with the tools we had. Usually, that consisted of a hammer and that pair of pliers I complained about. I often look back on that time in my life and draw from the forced “make do with what you have” situations it presented.
The advent of robotic process automation (RPA) has me reflecting on the challenges I faced during that time in my life. A few colleagues and I went through an exercise where we identified RPA use cases across the insurance value chain. In this article, we’ll cover how to identify the best use case for robotic process automation in insurance core processes.
You may have read our white paper, Taking a Business-Driven Approach to Continuous Improvement for Insurance Core Systems and Processes. We also will fill in some of the process gaps that may linger from a continuous improvement (CI) approach to your core systems.
But first, let’s define RPA.
What is Robotic Process Automation?
Robotic process automation, or RPA, doesn’t include an actual robot — at least not in the way we traditionally think of a robot with flashing lights and human-like attributes. But, when you consider the underlying concept, RPA may be closer to the traditional machine that springs to mind than it first seems.
Every fall at our farm, my family would build a temporary electric fence around our cornfields after harvest. The fence allowed our cattle to graze on those fields, and they could eat corn left on the ground. I hated building fences. It was hard. I would have to drive a fence post into the ground every 20 feet or so, usually in the freezing Nebraska rain.
Because my hands were as cold as ice, I constantly hammered my hands and I was not precise with the distance I put between the posts. I daydreamed about having my own personal robot to do the job for me. Now that would be some robotic process automation!
Is that fantasy that far off from what we know as RPA today? It’s completely different, right? Maybe not. Let’s consider for a moment why I wanted a robot to build my fence.
Building a fence was:
- Required endurance, and
- Needed to be completed quickly during the fall and winter months.
Adding to the desire for a robot was that I had a lot of other things going on when the fences needed built. But why did I dream about a robot, specifically? Why not some fence building system that was purpose-built for that task? That’s exactly what RPA is: technology that leverages software “robots” or artificial intelligence (AI) to automate processes, making them more efficient and enabling streamlined workflows.
When put in this context, modern RPA solutions really aren’t all that different. We yearn for a convenient and practical way to have an intelligent robotic assistant perform tasks in the way we would when we need to complete the tasks.
Let’s apply this to a present-day example. As a consultant in the insurance industry, I have had the opportunity to see RPA in action and have developed an understanding of where we can apply it. Amazingly, the same concepts from my robot daydreams on the farm still apply.
Identifying RPA Use Cases in Insurance
I am going to focus the remainder of this discussion on property and casualty (P&C) insurance core systems and how we can apply these concepts to identify and create effective RPA solutions.
RPA is building momentum in the insurance industry. We’ve started seeing a few insurance use cases show up repeatedly with RPA, such as during the certificate of insurance (CoI) process. These typically start with highly manual, repetitive tasks that either create bottlenecks or take away from the valuable time of key personnel.
While I think this is a great start, there are more examples with the potential to mature over time, such as at the front end of both the claim and quoting processes within the insurance value chain, first notice of loss (FNOL), claims processing, underwriting, and even back-office automation. The goal isn’t to replace humans, but to increase operational efficiency.
If you’ve ever tried to configure claim assignment in a popular core claim system like Guidewire’s ClaimCenter or Duck Creek Claims, you know it’s not an easy task. This is primarily because you don’t have all the information you need within the system to automate it, and it is difficult to validate whether a claim went to the right place.
Claim adjusters rarely feel they receive assignments fairly, and it can cause stress for all parties involved. Companies often directly tie efficiency metrics around closing claims and aging claims to performance measurement. To make matters worse, high volume and additional needs elsewhere exacerbate assignment challenges. System configuration solutions tend to be a little too abstract and don’t relate as directly to existing processes and assignment rationale.
In this example, RPA may be an effective solution.
Claim assignment is an intuitive action in that there are typically a set of steps a manager takes to determine when to start a claim. They check current workload, look at the attributes of the claim to determine which adjusters have the ability or certifications to work it, and they likely determine adjuster availability.
Robotic process automation can get you started with these simple interactions and increase in complexity to do tasks such as analyze personality matches. The goal is to automate the checks that bog down your claim manager and create bottlenecks. You can get initial assignments that the manager can adjust with an RPA bot.
Supercharging RPA With AI and Machine Learning
Once you’ve implemented RPA to automate non-value-added tasks, the question becomes how to add sophistication and grow the model. The use of artificial intelligence or machine learning (ML) can enhance your robotic process automation by adding the intelligence it takes it level up. When leveraged appropriately, these powerful processes can often surpass the analysis a human may apply to the situation, often due to time constraints or implications not evident to even the most astute people.
Applying an AI or ML algorithm is somewhat complex, but achievable with modern toolsets. It is a common and logical extension to many RPA scenarios. By adding AI or ML to your workflows, determinations or recommendations for assignments revolving around claim complexity or personality matching can happen quickly. This step reduces escalations and prevents a claim from bouncing around to several adjusters before an official assignment occurs.
This improvement allows employees to present their claim managers with data points and assignments, saving everyone time.
People often make assignments more complicated for the wrong reasons, causing most organizations to struggle with delivering a claim to the right adjuster quickly. RPA combined with AI or ML can be a great way to deliver meaningful improvement in this area.
Like claim assignments, the triage, assignment and initial evaluation of submissions can be an equally painful exercise for underwriting managers. Many carriers get more submissions than they can address, and they develop clogs in the triage and assignment process for submissions.
Initial activities, like checking the submission for completeness and requesting additional information from the agent can eat up valuable time for underwriters, managers or even underwriting assistants.
By applying RPA to the triage of submissions, you can start simply and add elements as you mature. Check for completeness, verify availability and consider including assistants (the human ones) appropriately. Once in place, AI or ML algorithms that focus on priority and potential authority needed can have significant impacts on where to direct underwriters. Underwriting managers can focus more on decision making than submission assignments.
Further sophistication can improve the involvement of marketing or risk control appropriately. Like the claim assignment example, these steps can be intuitive when considered in the context of the existing process, but unmanageable if you try to abstract it as part of your policy administration system configuration.
RPA Versatility in P&C Insurance
Both examples above highlight the versatility of an RPA solution. From a process perspective, they have a lot in common, but if you tried to implement a purpose-built technical solution, each would look different. You would add complexity and invest a lot of capital in a targeted and rigid solution.
An RPA solution is a flexible, versatile approach that closely aligns with steps and processes already in place or at least those desired. You can reuse similar process and actions, even if they are with different systems and teams.
Improve Operational Excellence With RPA
The adoption of robotic process automation (and subsequently, AI and machine learning) is becoming more prevalent for carriers. Simple, repeatable use cases are a great place to start RPA for P&C insurance claims.
Core systems should be a key focus area during your consideration of whether it makes sense to automate processes with RPA. These decisions are wrought with enhancement requests that sit in IT backlogs, often untouched. It’s okay to daydream a little and ask yourself, “What would I do if I had a robot? What would I have it do for me?” You might be surprised how achievable that daydream can be.