Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks

Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model's predictions in terms of Shapley additive explanations.

Automated summary

Predictive process monitoring (PPM) improves business process efficiency by predicting various aspects such as process outcome and next event. This study aims to enhance PPM research by incorporating external context information, specifically digital documents. The proposed approach processes digital documents using automated visual and textual feature extraction, and the evaluation shows significant performance improvements in predicting damage type, next event, and time until the next event.