Analysis of the Efficiency of Vehicle Insurance Claim Business Processes Using a Process Mining Approach: A Case Study
Abstract
The insurance claim submission process is one of the most critical components in the insurance industry, as it directly impacts both customer satisfaction and the operational efficiency of the company. A smooth and efficient claim process not only builds trust and loyalty among customers but also enhances the company’s ability to manage resources effectively. However, inefficiencies or bottlenecks in this process can lead to dissatisfaction, increased costs, and potential reputational damage. This study aims to analyze the efficiency of the vehicle insurance claim process at PT. X by leveraging the Process Mining approach, specifically utilizing the PM2 methodology to identify, evaluate, and address process inefficiencies. The research uses event logs collected from the company’s information system, providing detailed and objective data on the sequence and duration of activities involved in the claim process. By applying process mining techniques, this study uncovers key bottlenecks such as prolonged waiting times, deviations from standard operating procedures (SOPs), and unnecessary process loops that hinder optimal performance. Additionally, the research explores the root causes of these inefficiencies, including potential gaps in resource allocation, communication breakdowns, and outdated process workflows. The findings of the study offer significant insights for improving the insurance claim process. Evidence-based recommendations are proposed, such as the implementation of automated decision-making tools, improved monitoring systems for SOP compliance, and streamlining of redundant steps within the workflow. These recommendations aim to not only reduce processing time and enhance operational efficiency but also improve the overall customer experience.
References
Amoadu, M. (2023). Psychosocial factors, Perceived Well-Being, and Safety Incidents among Long-Distance Commercial Drivers in Ghaha (Doctoral dissertation, University of Cape Coast).
Andrews, K. (2023). Engineering a flexible data-centric process management system (Doctoral dissertation, Universität Ulm).
Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Maggi, F. M., Marrella, A., Mecella, M., & Soo, A. (2018). Automated discovery of process models from event logs: Review and benchmark. IEEE Transactions on Knowledge and Data Engineering, 30(4), 683–705. https://doi.org/10.1109/TKDE.2018.2841877
Badan Pusat Statistik. (2021). Statistik transportasi darat 2021. BPS Indonesia.
Bakir, N., El Nemar, S., Gehchan, F. B., Samrouth, K., & Vrontis, D. (2025). Optimizing airline services: leveraging data-driven strategies for enhanced customer satisfaction and engagement. Journal of Asia Business Studies. https://doi.org/10.1108/JABS-09-2024-0559
Bazaluk, O., Koriashkina, L., Cheberiachko, S., Deryugin, O., Odnovol, M., Lozynskyi, V., & Nesterova, O. (2022). Methodology for assessing the risk of incidents during passenger road transportation using the functional resonance analysis method. Heliyon, 8(11). https://doi.org/10.1002/hec.4553
Chigwende, S., & Govender, K. (2021). Customer satisfaction, loyalty and switching behaviour: A conceptual model of mobile telecommunications consumers. Turkish Journal of Computer and Mathematics Education, 12(9), 762-776.
Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2018). Fundamentals of business process management (2nd ed.). Springer.
Elleuch, M. (2021). Business process discovery from emails, a first step towards business process management in less structured information systems (Doctoral dissertation, Institut polytechnique de Paris).
Ellis, C. M., Grace, M. F., Smith, R. A., & Zhang, J. (2022). Medical cannabis and automobile accidents: Evidence from auto insurance. Health economics, 31(9), 1878-1897. https://doi.org/10.1002/hec.4553
Günther, C. W., & Van der Aalst, W. M. P. (2007). Fuzzy mining: Adaptive process simplification based on multi-perspective metrics. In G. Alonso, P. Dadam, & M. Rosemann (Eds.), Lecture notes in computer science: Vol. 4714. Business process management (pp. 328–343). Springer. https://doi.org/10.1007/978-3-540-75183-0_24
Heidari, F., & Assy, N. (2016). Usage analytics using process mining: Key findings for the Dutch Employee Insurance Agency. CEUR Workshop Proceedings.
Jamaluddin, F., Saibani, N., Mohd Pisal, S. M., Wahab, D. A., Hishamuddin, H., Sajuri, Z., & Khalid, R. M. (2022). End-of-life vehicle management systems in major automotive production bases in Southeast Asia: a review. Sustainability, 14(21), 14317. https://doi.org/10.3390/su142114317
La Rosa, M., Dumas, M., Uba, R., & Dijkman, R. (2019). Business process model and notation. Springer International Publishing.
Luthfiyani, F., & Ahyudanari, E. (2021). Karakteristik pengemudi sepeda motor dalam model peluang kecelakaan. Jurnal Aplikasi Teknik Sipil, 19(2), 151–158.
Maheshwari, P., Kamble, S., Belhadi, A., Mani, V., & Pundir, A. (2023). Digital twin implementation for performance improvement in process industries-A case study of food processing company. International Journal of Production Research, 61(23), 8343-8365. http://dx.doi.org/10.1080/00207543.2022.2104181
Mansur, S. S., Kurniati, A. P., Rojas, E., & Adiwijaya. (2022). Process mining to improve clinical pathways in breast cancer treatment using the Indonesia Health Insurance dataset. In Proceedings of the 2022 International Conference on Data Science and Its Applications (ICoDSA) (pp. 1–7). IEEE. https://doi.org/10.1109/ICoDSA55874.2022.9862851
Perusso, A., & Leal, R. (2022). The contribution of execution and workplace interaction to problem-based learning. The International Journal of Management Education, 20(1), 100596. https://doi.org/10.1016/j.ijme.2021.100596
Pramudya, A. A., Wibowo, A., Santosa, W., & Permana, F. J. (2025). A claim-based risk mapping of contractor's all-risk insurance in Indonesia. Construction Economics and Building, 25(1), 122-142. https://doi.org/10.5130/AJCEB.v25i1.9035
Sugarman, S. D. (2021). Tort damages for non-economic losses: Personal injury. In Comparative Tort Law (pp. 305-335). Edward Elgar Publishing. http://dx.doi.org/10.4337/9781789905984.00022
Suliyanto, A., Subrata, A., Parera, A., Suhendar, B., Dalimunthe, D. A. S., Novika, F., Padli, H., Indra, I. M., Awaloedin, M., Harahap, N., Maolani, R., Suhartono, & Ardiansyah, T. (2021). Pengetahuan seputar asuransi. Tahta Media Group.
Suriadi, S., Wynn, M. T., Ouyang, C., ter Hofstede, A. H. M., & Van Dijk, N. J. (2013). Understanding process behaviours in a large insurance company in Australia: A case study. In F. Daniel, J. Wang, & B. Weber (Eds.), Lecture notes in computer science: Vol. 7908. Business process management (pp. 449–464). Springer. https://doi.org/10.1007/978-3-642-38709-8_29
Susanto, A. (2022). Digital transformation of the insurance industry: the potential of insurance technology (insurtech) in Indonesia. Journal of Humanities, Social Sciences and Business (JHSSB), 2(1), 172-180. http://dx.doi.org/10.55047/jhssb.v2i1.375
Tetteh, L. A., Agyenim‐Boateng, C., & Simpson, S. N. Y. (2024). Institutional pressures and accountability processes in pursuit of sustainable development goals: Insights from Ghanaian indigenous oil companies. Corporate Social Responsibility and Environmental Management, 31(1), 89-107. https://doi.org/10.1002/csr.2554
Udeh, E. (2024). Examining The Impact of Operation And Production Management Failure On Customer Satisfaction And Organizational Growth: A Qualitative Study. European Journal of Political Science Studies, 7(1). http://dx.doi.org/10.46827/ejpss.v7i1.1716
Van der Aalst, W. M. (2016). Process mining: Data science in action. Springer.
Zhang, N., Cheng, P., Ning, P., Schwebel, D. C., & Hu, G. (2022). Conflicts between bus drivers and passengers in Changsha, China. Accident Analysis & Prevention, 169, 106623. https://doi.org/10.1016/j.aap.2022.106623
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