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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.

Predictive Process Monitoring

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. 


Apromore’s Predictive Process Monitoring


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:

Achieving end-to-end process transparency

Predictive analytics generated by Apromore’s Predictive Process Monitoring add-on are fed into dedicated dashboards, creating full transparency of your business processes. You can combine tactical and predictive analytics in a single dashboard to achieve full process visibility.  

Achieving end-to-end process transparency

Preventing frictions and other disruptions 

Sudden and unexpected frictions often happen as a part of normal business operations. Apromore’s Predictive Process Monitoring helps detect process frictions, irregularities and anticipate efficiency issues such as bottlenecks that can negatively impact your ability to meet your SLAs.

Preventing frictions and other disruptions

Enhancing customer experience 

The ability to predict customer interactions as well as their reactions to expected business process outcomes can help maximize value for your customers. Avoid customer frustrations caused by numerous and unnecessary requests for missing information and extra touch points that negatively affect your customer experience.  

Enhancing customer experience

Reliable resource planning  

The ability to estimate what activities will be performed next and in what order, as part of a running process case, is essential to the effective planning of resource workload. Use Apromore’s Predictive Process Monitoring to inform your resource allocation strategies, and to shift resources so you can attend to those cases that most need resources (high-priority cases) while balancing the workload between your resources.

Reliable resource planning


Hear from one of our customers: 

A large European insurance company needed to reduce the number of SLA violations in their motor vehicle claims handling process to ensure customer satisfaction and avoid expensive regulatory fines. They also wanted to improve their ability to balance the workload across claims handlers. The team built a predictive process monitoring dashboard to support claims managers in their decision-making, by providing a range of near real-time predictive analytics. These analytics included the remaining time to complete claims and the estimated completion date, the expected result (claim approved or rejected), the repair cost and the probability of violating SLAs at 30 and 60 days, depending on claim complexity. This was achieved by connecting Apromore’s Predictive Process Monitoring add-on to the insurance company’s claims management system, in order to inject into Apromore a stream of input events relating to claims being processed. Events were then fed into Apromore’s Predictive Process Monitoring add-on to generate predictive analytics, which were then channelled into the predictive dashboard. Via this solution, claims managers could monitor performance across a range of motor vehicle claims, and anticipate compliance and performance issues before they arose.

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