Robotic Process Automation for Insurance: Direct Bill Commissions Reconciliations
Do you really still have people performing these rote, error-prone chores?
“Reconciliation” is just a fancy word for “making sure things match.” It’s an essential cross-checking chore that can be found in all industries. In insurance, you need to make sure that numbers from one source—say a carrier’s commission statement—match those in another—such as your brokerage’s online platform.
That’s exactly what we’ll explore—and automate—in this article. Indeed, this article is a companion to this video, which provides a screen-capture recording of the robot in action:
Different carriers, different statements
Any brokerage receives commission statements—what it uses to help pay its agents—from its different carriers each month. And each month, a human must devote a lot of time to poring through each statement, line-by-line, and making sure that the “commissions paid” match what were previously calculated in the brokerage’s online booking platform. If they match, they get logged as such.
If they don’t match, that’s a red flag. It means it’s time to take action and check for errors, either on the brokerage’s side or the carrier’s side.
And do you think that all those different carriers have agreed on a standard format for their statements? Of course not. This compounds the complexity of the challenge.
So to recap: Each month, a worker at the brokerage gets a stack of uniquely formatted commission statements, in PDF form. Then it becomes a grueling “side-by-side” chore:
- Park the PDF on one side of your screen.
- Log into the online platform on the other side of your screen.
- Look at the PDF for the first commission and agent. Jot down the “commission paid”—or better yet, copy it to your computer’s clipboard.
- Find that same commission and agent in the online platform. Look at the projected commission.
- Check your notes. See if they match.
- If they match, type (or paste) in the correct number, and move on to the next.
- If they don’t, make a big note of that so you can address it afterward.
As you can see, this is time-consuming and error-prone. In fact, it takes the average knowledge worker (who’s hardly an “average” person; they’re highly trained and paid) about ten minutes to process just one statement. And given all the searching, noting, comparing, copying, and pasting, human workers generate a ten- to 15-percent error rate.
Insurance robotic process automation to the rescue
The Lab is able to configure a special robot—actually, just a small software program—to do everything that the human worker does, only faster and error-free.
Think of the robot as “just another worker,” only one that never tires, takes breaks, makes mistakes, or complains. It has its own log-in and password for the online system. And so it does just what the human did, only better:
- It doesn’t have to work line-by-line. It memorizes the entire commission-statement PDF first.
- Then it logs in, compares each commission, and pastes in the correct amount.
- If the amounts don’t match, it pastes them into an “Exception report” Microsoft Notepad document.
- It also creates a new Excel spreadsheet, logging all the info from its work. Isn’t that a nice touch for reporting?
But wait—what about all those different carrier-specific formats? Not a problem. The Lab simply configured different versions of the same bot to handle each one.
Real numbers, real results
The Lab can configure a robot like this, start to finish, in about two weeks. And where a human takes ten minutes, the bot does the work in 50 seconds! While humans generate a a ten- to 15-percent error rate, the bot’s error rate is zero.
And you might be surprised to learn that human workers love robots—because they free them up for higher-value activities.
Put the power of robotic process automation, or RPA, to work for your insurance operation—installed remotely, from our U.S. offices in Houston! Call The Lab at (201) 526-1200 or email firstname.lastname@example.org to book your no-obligation 30-minute screen sharing demo today.