DOI: https://doi.org/10.14710/vol15iss4pp504-511

Performance Measurement of Robotic Process Automation in Policy Issuance Activities in Insurance Companies

*M Ihza Mahendra Tampubolon orcid  -  Department of Software Information Systems, Master of Management Information Systems, Universitas Gunadarma, Jl. Margonda Raya 100, Depok, Indonesia 16424 | Universitas Gunadarma, Indonesia
Irwan Bastian  -  Information Systems Faculty of Computer Science and Information Technology, Gunadarma University, Depok, Indonesia
Dewi Agushinta R  -  Information Systems Faculty of Computer Science and Information Technology, Gunadarma University, Depok, Indonesia
Open Access Copyright (c) 2025 Jurnal Sistem Informasi Bisnis

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Abstract
The issuing policies process in the insurance industry is a complex activity that requires high precision, especially in verifying data and ensuring compliance with regulations. This process is often done manually, which takes a long time, is prone to errors, and is less efficient in handling high workloads. Robotic Process Automation (RPA) offers a solution to automate the policy issuance process to improve operational efficiency, reduce human errors, and speed up the process time. The research aims are to build RPA on the motor vehicle policy issuance process and measure its performance in comparison with the manual method. The research methods used include business process analysis, system design, RPA development using UiPath, testing, implementation, and performance evaluation based on five main variables: speed, availability, compliance, quality, and human resource efficiency measured by Full Time Equivalent (FTE). The results show that RPA can increase the efficiency of policy issuance by reducing processing time by 97.2%, from an average of 90 minutes per policy to 2 minutes 30 seconds. Regarding availability, RPA can operate 24/7 with an uptime of 99.9%. In addition, RPA result performance is 4,861 FTE in labor savings, ensuring compliance with regulations with a lower error rate than manual processes. However, certain cases still require manual intervention, especially in high-risk policies. The research results can serve as a reference for insurance companies in optimizing the implementation of RPA to enhance operational efficiency and provide faster and more accurate service to customers.
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Keywords: Compliance; efficiency; policy issuance; RPA

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Section: Research Articles
Language : EN
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  1. Abildtrup, A., 2024. The rise of robotic process automation in the banking sector: Streamlining operations and improving efficiency. Journal of Computing and Natural Science, 4(1), 31–40. https://doi.org/10.53759/181X/JCNS202404004
  2. Adam, F. F., & Hikmah, Y., 2022. Analysis of the Online and Offline Policy Issuance Process of a Life Insurance Company in Indonesia. Proceedings, 83(1), 14. https://doi.org/10.3390/proceedings2022083014
  3. Anagnoste, S., 2017. Robotic Automation Process - The next major revolution in terms of back office operations improvement. Proceedings of the International Conference on Business Excellence, 11(1), 676–686. https://doi.org/10.1515/picbe-2017-0072
  4. Bourgouin, A., Leshob, A., & Renard, L., 2018. Towards a Process Analysis Approach to Adopt Robotic Process Automation. 2018 IEEE 15th International Conference on E-Business Engineering (ICEBE), 46–53. https://doi.org/10.1109/ICEBE.2018.00018
  5. Costa, D., Mamede, H. S., & Mira da Silva, M., 2022. Robotic Process Automation (RPA) Adoption: A Systematic Literature Review. Engineering Management in Production and Services, 14, 1-12. https://doi.org/10.2478/emj-2022-0012
  6. Czarnecki, C., & Fettke, P., 2021. Robotic Process Automation: Management, Technology, Applications. Walter de Gruyter GmbH
  7. Deloitte, 2020. Calculating Real ROI on Intelligent Automation (IA). https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technologymedia-telecommunications/blue-prism-white-paper-final.pdf
  8. Durão, D., & dos Reis, A.P., 2024. How does robotic process automation create value for firms? Information Systems and E-Business Management, 22, 721–740. https://doi.org/10.1007/s10257-024-00685-z
  9. Enríquez, J. G., Jiménez-Ramírez, A., Domínguez-Mayo, F. J., & García-García, J. A., 2020. Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study. IEEE Access, 8(1), 39113–39129. https://doi.org/10.1109/ACCESS.2020.2974934
  10. Farinha, D., Pereira, R., & Almeida, R., 2024. A framework to support Robotic Process Automation. Journal of Information Technology, 39(1), 149-166. https://doi.org/10.1177/02683962231165066
  11. Fernandez, D., Tun, U., Onn, H., & Aman, A., 2018. Impacts of Robotic Process Automation on Global Accounting Services. March 2019. https://doi.org/10.17576/AJAG-2018-09-11
  12. Hadikusuma, R.S., Bijokangko, R.S., & Ananda, M.D., 2023. Analisis Perbandingan Software UiPath dan OpenRPA pada Robotic Processing Automation. Prosiding Seminar Nasional Sains dan Teknologi, 13(1), 320-325. https://doi.org/10.36499/psnst.v13i1.9531
  13. Issac, R., Muni, R., & Desai, K., 2018. Delineated Analysis of Robotic Process Automation Tools. Proceedings of the 2018 2nd International Conference on Advances in Electronics, Computers and Communications (ICAECC 2018), 0–4. https://doi.org/10.1109/ICAECC.2018.8479511
  14. Lacity, M., Craig, A., & Willcocks, L., 2015. Robotic Process Automation at Telefónica O2: Research on Business Services Automation. The Outsourcing Unit Working Research Paper Series, 2, 1–19
  15. Novian, C., Idah, Y. M., & Rifai, Z., 2022. Pemodelan Proses Bisnis Pengadaan Barang (Stok) Menggunakan Pendekatan Business Process Modelling Notation (BPMN): Studi Kasus: SHM Motor Purwokerto. Journal of Information System Management (JOISM), 3(2), 63 - 69. https://doi.org/10.24076/joism.2022v3i2.600
  16. Palaniappan, R., 2024. An Overview on Robotic Process Automation: Advancements, Design Standards, its Application, and Limitations. Informatica, 48. https://doi.org/10.31449/inf.v48i1.5058
  17. Patil, S., Mane, V., & Patil, P., 2019. Social innovation in education system by using robotic process automation (Rpa). International Journal of Innovative Technology and Exploring Engineering, 8(11), 3757–3760. https://doi.org/10.35940/ijitee.K2148.0981119
  18. Ratia, M., Myllärniemi, J., & Helander, N., 2018. Robotic process automation - Creating value by digitalizing work in the private healthcare sector? ACM International Conference Proceeding Series, 222–227. https://doi.org/10.1145/3275116.3275129
  19. Romao, M., Costa, J., & Costa, C. J., 2019. Robotic Process Automation: A Case Study in the Banking Industry. Iberian Conference on Information Systems and Technologies (CISTI 2019), 1–6. https://doi.org/10.23919/CISTI.2019.8760733
  20. Sigurðardóttir, G.L., 2018. Robotic Process Automation: Dynamic Roadmap for Successful Implementation. Reykjavík University
  21. The Insurance Universe. (n.d.). Essential Guide to Effective Policy Issuance Procedures. Diakses pada 8 Oktober 2024, dari https://theinsuranceuniverse.com/policy-issuance-procedures/
  22. Van der Aalst, W.M.P., Bichler, M., & Heinzl, A., 2018. Robotic Process Automation. Business & Information Systems Engineering, 60, 269–272. https://doi.org/10.1007/s12599-018-0542-4
  23. Vuksic, V. B., 2019. Robotic Process Automation : Systematic Literature Review. August. https://doi.org/10.1007/978-3-030-30429-4
  24. Wewerka, J., & Reichert, M., 2020. Towards Quantifying the Effects of Robotic Process Automation. Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW, 2020-Octob, 11–19. https://doi.org/10.1109/EDOCW49879.2020.00015
  25. Wilson, M., Wnuk, K., Silvander, J., & Gorschek, T., 2018. A Literature Review on the Effectiveness and Efficiency of Business Modeling. https://doi.org/10.5277/e-Inf180111
  26. Yatskiv, N., Yatskiv, S., & Vasylyk, A., 2020. Method of Robotic Process Automation in Software Testing Using Artificial Intelligence. 2020 10th International Conference on Advanced Computer Information Technologies (ACIT 2020), 501–504. https://doi.org/10.1109/ACIT49673.2020.9208806

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