Predictive Process Monitoring

Achieving operational and customer excellence requires you to forecast and preempt issues before they arise. Will the customer accept the quote you just sent out? Will the customer lodge a complaint? Will the customer get their issues resolved on time? Will the customer accept a loan offer or an insurance claims settlement? 

All these questions can be answered with real-time predictions. Modern AI technology allows us to make such predictions accurately and reliably using historical data. However, to get there, you need to build prediction models and get predictions out of these models. Normally, building such models requires several weeks or months of effort, including hard-to-find data scientists and other IT specialists. 

With Apromore’s Predictive Process Monitoring add-on, you can build predictive models for a variety of business process prediction problems using a point-and-click interface without writing a single line of code. For example, you can train a model to predict the remaining time of each case or to predict the outcome of a process – Will the customer complain? Will we meet our response times?  

You can use these predictions to assist live operations and to plan daily work by identifying timely interventions that can help prevent unnecessary roadblocks and Service Level Agreement (SLA) violations, improve efficiency and compliance, and enhance customer experience.

What Is Apromore’s Predictive Process Monitoring and How Can Organizations Benefit from It

Simply put, predictive process monitoring acts as a ‘crystal ball’ to a business process, by enabling process stakeholders to estimate the future of each process case currently running. One can predict the outcome of each process instance, whether the instance will be affected by potential delays or lead to SLA violations, what activities will be executed next and what the customer reaction might be to expected outcomes. For example, claims managers can use Apromore’s Predictive Process Monitoring add-on to predict, on a claim-by-claim basis, whether the handling SLA of 30 days will be met, whether the claim will be approved and with what amount, and what activities will likely be performed until the claim is fully handled. 

To make accurate and reliable estimates, Apromore employs machine learning algorithms and combines these with a range of process-specific feature engineering and prediction bucketing techniques.  

The goal is to provide accurate, stable predictions that can be presented to managers and process workers as early as possible, so they can minimize the time to intervention. 

Predictive process monitoring is thus about near real-time analysis, making predictions as running process cases unfold. As such, it complements tactical process mining capabilities like automated discovery and performance mining, with an operational support capability. 

How does it work?

Apromore’s Predicting Process Monitoring add-on includes a “training” module and a “runtime monitoring” module. The training module uses machine learning algorithms to build predictive models from an event log. With a few clicks, you can build predictive models from an event log containing historical cases. 

Once you have trained a model, Apromore provides you with reports about the model’s predictive power and explanations of how the model makes its predictions. 

The trained model can then be used to generate predictive dashboards. A predictive dashboard gives you predictions for each open case in the process. For example, it may tell you that one of the open (ongoing) cases will take two more days to complete or that it will result in a negative outcome, such as a customer complaint. 

Key Benefits of Apromore’s Predictive Process Monitoring 

Predictive Process Monitoring capabilities enable analysts and decision-makers to forecast business process outcomes easily and effectively, benefiting from: