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Predictive Analytics for Semi-structured Case Oriented Business Processes

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This study explores the application of predictive analytics in semi-structured case-oriented business processes, specifically within the automobile insurance claims handling scenario. Utilizing an Ant Colony Optimization (ACO)-based algorithm, we developed a probabilistic activity graph to pinpoint critical decision points in the process. Our findings suggest that this approach can effectively predict immediate, intermediate, and final outcomes based on available data, even when complete information is lacking. The results showcase the utility of identifying decision points without needing to construct a formal process model.

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Predictive Analytics for Semi-structured Case Oriented Business Processes

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  1. Predictive Analytics for Semi-structured CaseOrientedBusinessProcesses Kerry Lumi Tartu 2013

  2. Semi-structured CaseOrientedBusinessProcess Business process management systems typically includerestrictions such as rigid control flow and context tunneling Semi-structuredbusinessprocesseslifecycle is not fully driven by a formal process model The execution of a semi-structured process is not completely controlled by a central entity Case oriented processes are an example of semi-structuredbusinessprocesses.

  3. Scenario Automobileinsuranceclaimshandlingscenario Thescenariohas been simplified for the sake of achieving clarity in our experiments and results

  4. Implementation Anant-colonyoptimization (ACO) based algorithm wasappliedto create a probabilistic activitygraph from traces, and use it to identify key decision points in a given process. Usingstandard decision tree learning algorithm likelihood of different outcomes from the nodecan be correlated with the contents of documents accessed by the activitynode.

  5. Conclusion Resultson an automobile insurance industry claims scenario indicate thatthisapproach can be useful for predicting outcomes that immediately followa given decision point, final outcomes, and intermediate outcomes thatoccur between immediate and final outcomes Furthermore our experiments indicatethat thisapproach can be useful for predicting outcomes of decisions insituations where not all the data values necessary to make a decision are available Thisapproach also demonstrates a way to identify decision points in a semi-structured process using a probabilistic graph without necessarily mininga process model to represent the process

  6. Thankyou!

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