Model Pengukuran Risiko Operasional Perusahaan Asuransi Umum Syariah Menggunakan Metode Analytic Network Process
Abstract
Objectives: The operational risk measurement model, which generally employs a quantitative approach, is deemed insufficient to capture all essential aspects for an adequate model. This research develops a comprehensive model by integrating risk factors, stakeholders, loss event data, and alternative mitigation strategies, tailored explicitly to Islamic general insurance companies.
Design/method/approach: This study employs a qualitative approach, incorporating expert opinions and utilizing the Analytical Network Process (ANP) to evaluate risk criteria weights. Operational risks are identified using seven loss event categories based on Basel II and ORIC standards.
Results/findings: The findings indicate that the priority order of operational risk loss events is clients, products, and business practices; internal fraud; and business disruptions and system failures. A key recommendation for enhancing risk measurement is the improvement of standard recording processes for all risks through an integrated information system.
Theoretical contribution: This research resulted in a more reliable model for measuring operational risks and making a significant contribution to the industry.
Practical contribution: This research offers key insights for regulators in developing operational risk measurement policies.
Limitations: This research was conducted with a qualitative approach, which can give rise to the subjectivity of expert opinions and potentially ignore unidentified risks.
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Abdullah, M., Shahimi, S., & Ghafar Ismail, A. (2011). Operational risk in Islamic banks: examination of issues. Qualitative Research in Financial Markets, 3(2), 131–151. https://doi.org/10.1108/17554171111155366
Acharyya, M. (2012). The Scope of Developing Optimization Models for Insurer’s Operational Risk from Risk-Return Trade-Off Perspective.
Ascarya. (2005). Analytic Network Process (ANP) Pendekatan Baru Studi Kualitatif. In Seminar Intern Program Magister Akuntansi Fakultas Ekonomi di Universitas Trisakti,.
Ascarya, Rahmawati, S., & Sukmana, R. (2016). Measuring the Islamicity of Islamic Bank in Indonesia and Other Countries Based on Shari’ah Objectives. Proceeding 11th International Conference on Islamic Economics and Finance, October, 1–38.
Berger, A. N., Curti, F., Mihov, A., & Sedunov, J. (2022). Operational Risk is More Systemic than You Think: Evidence from U.S. Bank Holding Companies. Journal of Banking and Finance, 143, 106619. https://doi.org/10.1016/j.jbankfin.2022.106619
Brandts, S. (2004). Operational Risk and Insurance: Quantitative and Qualitative Aspects. https://doi.org/10.2139/ssrn.493082
Chavez-Demoulin, V., Embrechts, P., & Neslehova, J. (2006). Quantitative models for operational risk. Journal of Banking & Finance, 30(10), 2635–2658. http://e-citations.ethbib.ethz.ch/view/pub:10679
Chernobai, A., & Rachev, S. T. (2007). Applying Robust Methods to Operational Risk Modeling.
Chernobai, A. S., Rachev, S. T., & Fabozzi, F. J. (2007). Operational Risk A Guide to Basel II Capital Requirements, Models, and Analysis. In John Wiley & Sons, Inc. https://doi.org/10.2139/ssrn.762425
Cornwell, N., Bilson, C., Gepp, A., Stern, S., & Vanstone, B. J. (2023). Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study. Pacific Basin Finance Journal, 79(March), 102011. https://doi.org/10.1016/j.pacfin.2023.102011
Cruz, M., Coleman, R., & Salkin, G. (1998). Modeling and measuring operational risk. Journal of Risk, 63–72.
Cruz, M. G., Peters, G. W., & Shevchenko, P. V. (2015). Fundamental Aspects of Operational Risk and Insurance Analytics A Handbook of Operational Risk.
Darmawan, A. (2014). Design of Operational Risk Measurement in Consumer Finance Companies used Risk Breakdown Structure (RBS) and Analytic Network Process (ANP) Methods. International Conference on Industrial Engineering and Operations Management, 186–195.
Darmawan, A., Farizal, & Prajadhiana, D. (2014). Design of Operational Risk Measurement in Consumer Finance Companies used Risk Breakdown Structure (RBS) and Analytic Network Process (ANP) Methods. International Conference on Industrial Engineering and Operations Management, 186–195.
Ebnöther, S., Vanini, P., McNeil, A., & Antolinez-Fehr, P. (2001). Modelling Operational Risk. Operational Risk Version, June 2001, 1–23. https://doi.org/10.2139/ssrn.293179
Eckert, C., Gatzert, N., & Heidinger, D. (2020). Empirically assessing and modeling spillover effects from operational risk events in the insurance industry. Insurance: Mathematics and Economics, 93, 72–83. https://doi.org/10.1016/j.insmatheco.2020.04.003
Eling, M., & Wirfs, J. (2018). What are the actual costs of cyber risk events? European Journal of Operational Research, 272(3), 1109–1119. https://doi.org/10.1016/j.ejor.2018.07.021
Farsiah, L. (2024). Model Pengukuran dan Strategi Mitigasi Risiko Operasional Perusahaan Asuransi Umum Syariah di Indonesia.
Farsiah, L., Amalia, E., Saharuddin, D., & Lukman, L. (2024). Is the hybrid method more adequate for measuring operational risk? Journal of Accounting and Investment, 25(1), 152–171. https://doi.org/10.18196/jai.v25i1.20660
G.L.Overton, J.B.Orr, & Hitchcox, A. N. (2004). Quantifying Operational Risk In General Insurance Companies . British Actuarial Journal, 10(5), 1013–1026. https://doi.org/10.1017/S1357321700002920
Gatzert, N., & Kolb, A. (2012). Risk measurement and management of operational risk in insurance companies from an enterprise perspective. https://doi.org/10.1111/j.1539-6975.2013.01519.x
Habachi, M., & Benbachir, S. (2020). The Bayesian Approach to Capital Allocation at Operational Risk : A Combination of Statistical Data and Expert Opinion. International Journal of Financial Studies, 8(9), 1–25.
Kato, T. (2012). Quantitative Operational Risk Management: Properties of Operational Value at Risk (OpVaR). 91–112. http://www.math.ritsumei.ac.jp/crest/Kato.pdf
Kheybari, S., Rezaie, F. M., & Farazmand, H. (2020). Analytic network process: An overview of applications. Applied Mathematics and Computation, 367. https://doi.org/10.1016/j.amc.2019.124780
Md Sum, R. (2018). Using Mathematics to Quantify Subjective Decisions: Application of Analytic Hierarchy Process to Risk Assessment. Journal of Advanced Research Design, 44(1), 7–19. www.akademiabaru.com/ard.html
Mwangi, M. M. (2017). Operational Risk Modeling for General Insurance Companies in Kenya.
Orkut, L., Wang, M., III, R. T. P., & Siyi Luwp, V. H. C. (2013). Quantitative Modelling of Operational Risk. Risk Management, 51(28), 27–31.
Oscar Akotey, J., & Abor, J. (2013). Risk management in the Ghanaian insurance industry. Qualitative Research in Financial Markets, 5(1), 26–42. https://doi.org/10.1108/17554171311308940
Pena, A., Patino, A., Chiclana, F., Caraffini, F., Gongora, M., Gonzalez-Ruiz, J. D., & Duque-Grisales, E. (2021). Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events. Applied Soft Computing, 107, 107381. https://doi.org/10.1016/j.asoc.2021.107381
Rusydiana, A. S., & Devi, A. (2017). Development Strategy of Micro-takaful Institution: Case Study Working Group Indonesia. 16(2), 265–278. https://doi.org/10.15408/etk.v16i2.5267
Saaty, T. L. (2004). Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network. Journal of Systems Science and Systems Engineering, 13(2), 129–157. https://doi.org/10.1007/s11518-006-0158-y
Saaty, T. L., & Vargas, L. G. (2006). Decision Making With the Analytic Process Network Process. In Springer (Vol. 95). http://www.amazon.com/dp/0387338594
Smithson, C. (2000). Quantifying Operational Risk. Risk, March, 1–5.
Taherdoost, H., & Madanchian, M. (2023). Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations. Journal of Data Science and Intelligent Systems, 1(1), 12–18. https://doi.org/10.47852/bonviewjdsis3202885
Torre-Enciso, M. I. M., & Barros, R. H. (2013). Operational Risk Management for Insurers. International Business Research, 6(1), 1–11. https://doi.org/10.5539/ibr.v6n1p1
Tripp, M. H., Bradley, H. L., Devitt, R., Orros, G. C., Overton, G. L., Pryor, L. M., & Shaw, R. A. (2004). Quantifying Operational Risk in General Insurance Companies. British Actuarial Journal, 10(5), 919–1012. https://doi.org/10.1017/s1357321700002919
Wang, Y., Li, J., & Zhu, X. (2017). A Method of Estimating Operational Risk: Loss Distribution Approach with Piecewise-defined Frequency Dependence. Procedia Computer Science, 122, 261–268. https://doi.org/10.1016/j.procs.2017.11.368
Wang, Z. L., Kim, J., Selvachandran, G., Smarandache, F., Hoang Son, L., Abdel-Basset, M., Thong, P. H., & Ismail, M. (2019). Decision making methods for evaluation of efficiency of general insurance companies in Malaysia: A comparative study. IEEE Access, 7, 160637–160649. https://doi.org/10.1109/ACCESS.2019.2950455
Wei, R. (2003). Operational Risks in the Insurance Industry (Issue February).
Wyman, O., & International, O. (2015). Operational Risk Management & Measurement (Issue March). http://www.oliverwyman.com/content/dam/oliver-wyman/global/en/2015/may/Operational-Risk-Management-Measurement.pdf
Xie, S. (2023). Modelling auto insurance Size-of-Loss distributions using Exponentiated Weibull distribution and de-grouping methods. Expert Systems with Applications, 231(January), 120763. https://doi.org/10.1016/j.eswa.2023.120763
DOI: https://doi.org/10.24853/jago.5.2.182-202
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