To the folder that contains the activity log files, which are generated each day with no additional configuration needed as the system automatically determines the structure of the output to ingest existing and future activities, data..
Copilot automatically analyzes the data to provide us with insights into the data to ensure we selected the right data set for analysis, and even helps to automatically map the data.
To the required fields based on the analysis. Once analyzed, we can see the interactive dashboard of our claims process with KPIs, process map and Copilot..
Even as a process mining novice, I can quickly get top insights from Copilot in a conversational manner. It tells me assigned handlers is the longest running activity,.
And focusing on Variant 12, as well as changing to performance view, we can see that nothing happens for a long time after the handler is assigned and the process resets and goes back to.
The coverage checks step after a day or two. This is a great insight. Not only can Copilot, help me uncover insights and identify bottlenecks, it can also provide.
Suggestions that help address these bottlenecks. One suggestion is a Power App that allows customer service reps to assign handlers more efficiently based on their caseload..
Clicking on the suggestion takes us to the Power Apps Copilot experience, where it uses the app suggestion from process mining to generate the actual table and Dataverse as well as.
The intervase that connects to the table using natural language. Now all we need to do is connect the table to the underlying system. With Power Automate process mining,.
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Not only do we have a great out-of-box
Dashboard experience coupled with Copilot, but we also have advanced analytics capabilities with the same great Copilot experience. The process compare module allows us.To compare different variations of the process, like the claims with on time payment versus those that were paid late. We can visualize various metrics and not only look at raw numbers, but also percentages..
We notice late payments have a much higher frequency of escalation at 54 percent. We can even drill into more detail and find that 68 percent of claims paid late were escalated, while on time, it's only 14 percent..
We can still chat with Copilot on recommendations based on our goals, like predicting late payments, where it suggests doing a root cause analysis first and then.
Building a predicted model using the historical payment data as training sample. Using the root cause analysis module, we can see what factors influence late payments. It shows me that region is the most influential factor,.
Where a subset of 30 regions accounts for the majority of late payments. I can look at other influential factors like claim type, and I can continue to drill down, and within those 30 regions,.
Lower amounts actually contribute to a higher likelihood of late payments. Now I can export the relevant data from the tool and import it into Dataverse to train.
A late payment prediction model using AI builder. Zooming into the settlement part of the claims process, we can look at how process mining is able to support automation using power, automate, and task mining..
Here, Copilot recommends creating a Cloud flow for settlement approval. By clicking on the suggestion, we're taking to the natural language to flow creation experience.
Where we can see what Copilot
Generated for us as suggested flows. This is a good starting point, but we need more information on how the approval is conducted..For this, we can use task mining. Task mining allows us to record individual steps on a user's desktop machine and convert it into a process map of that task where we can see.
The sequence and details of the approval task across multiple users. Not only were we able to see the steps, but the solution even includes an AI-powered recommendation engine that uses all of.
The recorded meta data and recommends Power Automate connectors that can be used in the automation. By incorporating the invoice approval flow that Copilot recommended with the specific actions.
From task mining and AI connector recommendations, we're now able to create the full automation flow for settlement approval. Settlement PDF extraction and matching the data to the entries within.
Multiple systems is now automated. If the values match, it will automatically approve and send a notification in Teams. Now that we have discovered and acted upon.
The many improvement opportunities through process mining, let's use that data to build out a custom Power BI dashboard to monitor issues and track improvements..
We can publish the process directly into our existing Power BI workspace. Once the process is linked, we can navigate directly to the generated report in that workspace..
From here, we can edit the report and access all of the data that was generated by process mining. I'm able to create custom reports for compliance and automation and track all the relevant KPIs..
We can even add those metrics from the report into a scorecard through Power BI goals and set up alerts to notify the team when a Power BI goal has been reached. After some time, we receive notification and teams.
