Model Pengambilan Keputusan Underwriting Bisnis Kargo Laut
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Keywords

Underwriting

Technology
Marine Cargo
Insurance

How to Cite

Model Pengambilan Keputusan Underwriting Bisnis Kargo Laut. (2024). TeknoIS : Jurnal Ilmiah Teknologi Informasi Dan Sains, 14(1), 119-134. https://doi.org/10.36350/jbs.v14i1.238

Abstract

Faktor-faktor tambahan dapat dipertimbangkan untuk mendukung keputusan perusahaan asuransi untuk membuat penilaian risiko atau proses underwriting menjadi lebih cepat dan tepat. Tugas ini mengharuskan underwriter untuk cukup analitis, terorganisir dengan baik, dan akurat untuk membuat keputusan yang tepat untuk menyetujui atau menolak permohonan yang berisiko. Untuk itu diperlukan adanya teknologi yang dapat mendukung proses underwriting atau penilaian risiko. Penelitian ini berfokus pada underwriting asuransi kargo laut, yaitu terkait dengan fitur teknologi underwriting dalam menentukan tarif premi. Penelitian ini bertujuan untuk meningkatkan kecepatan dan ketepatan proses underwriting. Dengan menggunakan pendekatan Design Science Research, model dikembangkan dalam enam tahap. Temuan penelitian ini meliputi model konseptual dan prototipe model pengambilan keputusan underwriting untuk bisnis kargo laut. Berdasarkan hasil pengujian fitur prediksi tarif premi menggunakan model regresi linier dengan nilai R2 sebesar 0,993 dan MAE sebesar 0,0002727. Jadi dapat disimpulkan bahwa model ini dapat memenuhi kebutuhan pengguna.

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References

Aggour K. S., Bonissone, P. P., Cheetham, W. E., & Messmer, R. P. (2006). Automating the underwriting of insurance applications. AI magazine, 27(3), 36-36.

Alavi, M. & leidner, D. E. (2001). Knowledge Management and Knowledge Management Systems, MIS Quarterly, 25 (1), 107-136.

Arora N. and Vij S. A hybrid neuro-fuzzy network for underwriting of life insurance. International Journal of Advanced Research in Computer Science, pages 231–236, 2012.

Aswani, R., Ghrera, S. P., Chandra, S., & Kar, A. K. (2020). A hybrid evolutionary approach for identifying spam websites for search engine marketing. Evolutionary Intelligence (0123456789). 10.1007/s12065-020-00461-1 .

Ayuso, M., Guillen, M., & Marín, A. M. P. (2016). Using GPS data to analyse the distance travelled to the first accident at fault in pay-as-you-drive insurance. Transportation research part C: emerging technologies, 68, 160-167.

Bacry, E., Gaïffas, S., Leroy, F., Morel, M., Nguyen, D. P., Sebiat, Y., & Sun, D. (2020). SCALPEL3: A scalable open-source library for healthcare claims databases. Interna- tional Journal of Medical Informatics, 141 (May). 10.1016/j.ijmedinf.2020.104203 .

Barnum, C. M. (2020). Usability testing essentials: ready, set... test!. Morgan Kaufmann.

Barry, L., & Charpentier, A. (2020). Personalization as a promise: Can big data change the practice of insurance? Big Data and Society, 7 (1). 10.1177/2053951720935143 .

Baskerville, R., Richard, Pries-Heje, J., Jan, Venable, J., & John. (2009). Soft design science methodology. In DESRIST ’09: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology (pp. 1–11). ACM. https://doi.org/10.1145/1555619.1555631

Batra, J., Jain, R., Tikkiwal, V. A., & Chakraborty, A. (2021). A comprehensive study of spam detection in e-mails using bio-inspired optimization techniques. Inter- national Journal of Information Management Data Insights, 1 (1), Article 100006. 10.1016/j.jjimei.2020.100006 .

Bhalla A.,Enhancement in Predictive Model for Insurance Underwriting,International Journal of Computer Science & Engineering Technology (IJCSET) Vol. 3 No. 5, 5 May 2012 160

Biddle, R., Liu, S., Tilocca, P., & Xu, G. (2018). Automated underwriting in life insurance: Predictions and optimisation. In Databases Theory and Applications: 29th Australasian Database Conference, ADC 2018, Gold Coast, QLD, Australia, May 24-27, 2018, Proceedings 29 (pp. 135-146). Springer International Publishing.

Blackstone, E. H. (2013). Generating new knowledge in cardiac interventions. Anesthesi- ology Clinics, 31 (2), 217–248. 10.1016/j.anclin.2012.12.006 .

Capgemini, "World Insurance Report 2020", Capgemini, 2020.

Chakraborty, A., & Kar, A. K. (2017). Swarm intelligence: A review of algo- rithms. Modeling and Optimization in Science and Technologies , 10, 475–494. https://doi.org/10.1007/978-3-319-50920-4_19

Chakravarty, T., Ghose, A., Bhaumik, C., & Chowdhury, A. (2013, December). MobiDriveScore—A system for mobile sensor based driving analysis: A risk assessment model for improving one's driving. In 2013 Seventh International Conference on Sensing Technology (ICST) (pp. 338-344). IEEE

Chowdhury, S., Mayilvahanan, P., & Govindaraj, R. (2020). Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning in- tegrated with the internet of things. International Journal of Computers and Applications, 0 (0), 1–13. 10.1080/1206212X.2020.1733307.

Cebulsky M, Günther J., Heidkamp P., Brinkmann F., “The Digital Insurance – Facing Customer Expectation in a Rapidly Changing Worldâ€, Digital Marketplaces Unleashed pp 359-370, German, 2017

C. Eckert,K. Osterrieder , “How digitalization affects insurance companies: overview and use cases of digital technologiesâ€, ZVersWiss, German, 2020

Davenport, T. H., & Ronanki, R. (2018, January). Artificial Intelligence for the Real World. Harvard Business Review.

Das, D., Chakraborty, C., & Banerjee, S. (2020). A Framework development on big data analytics for terahertz healthcare. terahertz biomedical and healthcare technologies. Elsevier Inc. 10.1016/b978-0-12-818556-8.00007-0 .

Das, S., Datta, S., Zubaidi, H. A., & Obaid, I. A. (2021). Applying interpretable machine learning to classify tree and utility pole related crash injury types. IATSS Research . 10.1016/j.iatssr.2021.01.001 .

Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications, 115(9), 31-41.

Dave, H. S., Patwa, J. R., & Pandit, N. B. (2021). Facilitators and barriers to partici- pation of the private sector health facilities in health insurance & government-led schemes in India. Clinical Epidemiology and Global Health, 10 (January), Article 100699. 10.1016/j.cegh.2021.100699 .

Doupe, P., Faghmous, J., & Basu, S. (2019). Machine learning for health services re- searchers. Value in Health, 22 (7), 808–815. 10.1016/j.jval.2019.02.012 .

Doultani M. "Smart Underwriting – A Personalilses Virtual Agent", ," Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021)â€, IEEE Xplore Part Number: CFP21K74-ART; ISBN: 978-0-7381-1327-2

Dubey A, Parida T, Birajdar A, Prajapati AK, Rane S, “Smart Underwriting System: An Intelligent Decision Support System for Insurance Approval & Risk Assessmentâ€, 3rd International Conference for Convergence in Technology, The Gateway Hotel, XION Complex, Wakad Road, Pune, India. Apr 06-08, 2018

Eling M., Lehmann M, “The impact of digitalization on the insurance value chain and the insurability of risksâ€, The International Association for the Study of Insurance Economics, Geneva, 2017

Fidelia P., Application Of Machine Learning For Estimating Motor Vehicle Insurance Premium, tesis KCA University, 2019

Guggenberger, T., Kuhn, M. & Schellinger, B., 2021. Insured? Good! Designing a Blockchain-based Credit Default. MENACIS2021, p.4.

Gupta, S., Kar, A. K., Baabdullah, A., & Al-Khowaiter, W. A. A (2018). Big data with cog- nitive computing: A review for the future. International Journal of Information Manage- ment, 42 , 78–89 (April). 10.1016/j.ijinfomgt.2018.06.005 .

Handel, P., Skog, I., Wahlstrom, J., Bonawiede, F., Welch, R., Ohlsson, J., & Ohlsson, M. (2014). Insurance telematics: Opportunities and challenges with the smartphone solution. IEEE Intelligent Transportation Systems Magazine, 6(4), 57-70.

Hasibuan Z., Metodologi Penelitian Pada Bidang Ilmu Komputer Dan Teknologi informasi, 2007.

IBM Global Business Services, “Integrating the value of data in the underwriting process- An underwriting point of viewâ€, White Paper, 2012.

IAIS, 2018. Issues Paper on Increasing Digitalisation in Insurance and its Potential Impact on Consumer Outcomes. International Association of Insurance Supervisor.

Inmon, W.H., Building The Data Warehouse, Wiley Publishing, 2005

Joram MK, Harrison BK, Joseph N, “A Knowledge-Based System for Life Insurance Underwritingâ€, Information Technology and Computer Science, 2017, 3, 40-49

Kakhki, F. D., Freeman, S. A., & Mosher, G. A. (2020). Applied machine learning in agro- manufacturing occupational incidents. Procedia Manufacturing, 48 , 24–30 (2019). 10.1016/j.promfg.2020.05.016 .

Kacelan V., Kacelan L., and Buric M. N. A nonparametric data mining approach for risk prediction in car insurance: a case study from the montenegrin market. Economic Research- Ekonomska Istraivanja, pages 545–558, 2017.

Kar, A. K. (2016). Bio inspired computing - a review of algorithms and scope of applica- tions. Expert Systems with Applications, 59 , 20–32. 10.1016/j.eswa.2016.04.018 .

Kasy, M. (2018). Optimal taxation and insurance using machine learning —Sufficient statistics and beyond. Journal of Public Economics, 167 , 205–219. 10.1016/j.jpubeco.2018.09.002

Kašćelan, V., Kašćelan, L., & Novović Burić, M. (2016). A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market. Economic research-Ekonomska istraživanja, 29(1), 545-558.

Kaur, P., Sharma, M., & Mittal, M. (2018). Big data and machine learning based secure healthcare framework. Procedia Computer Science, 132 , 1049–1059. 10.1016/j.procs.2018.05.020 .

Karhade, A. V., Ogink, P. T., Thio, Q. C. B. S., Broekman, M. L. D., Cha, T. D., Hersh- man, S. H., …, & Schwab, J. H. (2019). Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion. Spine Journal, 19 (6), 976–983. 10.1016/j.spinee.2019.01.009 .

Kalske M,"Transforming monolithic architecture towards microservice architecture",Thesis UNIVERSITY OF HELSINKI, 2017

Khan, F. H. , Bashir, S. , & Qamar, U. (2014). Author ’ s personal copy TOM : Twitter opinion mining framework using hybrid classi fication scheme. Decision Supp. Syst., 57 (January), 245–257 .

Knighton, J., Buchanan, B., Guzman, C., Elliott, R., White, E., & Rahm, B. (2020). Pre- dicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: Exploring the roles of topography, minority populations, and political dissimilarity. Journal of Environmental Management, 272 , Article 111051. 10.1016/j.jenvman.2020.111051 .

Kose, I., Gokturk, M., & Kilic, K. (2015). An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing Jour- nal, 36 , 283–299. 10.1016/j.asoc.2015.07.018 .

Kraus, M., Feuerriegel, S., & Oztekin, A. (2020). Deep learning in business analytics and operations research: Models, applications and managerial implications. European Jour- nal of Operational Research, 281 (3), 628–641. 10.1016/j.ejor.2019.09.018 .

Krovvidy, S. (2008). Custom DU: A Web-Based Business User-Driven Automated Underwriting System. AI Magazine, 29(1), 41-41.

Kunreuther, H., Meszaros J., Hogarth R., Spranca M.: Ambiguity and Underwriter Decision Processes. Journal of Economic, Behavior and Organization 26, 337-352 (1995).

Kumar, R. (2019). Research Methodology (Fifth). Los Angelos: SAGE

Larson, W. D., & Sinclair, T. M. (2021). Nowcasting unemployment insurance claims in the time of COVID-19. International Journal of Forecasting xxxx. 10.1016/j.ijforecast.2021.01.001 .

Lamsweerde A.V., "Goal-oriented requirements engineering: a guided tour", "vol. 249, no. August. IEEE Comput. Soc", 2001, pp. 249–262.

Lamsweerde A.V., Letier E. , “From object orientation to goal orientation: A paradigm shift for requirements engineering,†Radical Innovations of Software and Systems Engineering in the Future, vol. 2941, no. I, pp. 325–340, 2004.

Lawrence, C., Tuunanen, T., & Myers, M. (2010). Extending Design Science Research Methodology for a Multicultural World. IFIP Advances in Information and Communication Technology (Vol. 318). Springer. https://doi.org/10.1007/978-3-642-12113-5_7

Lesage L, Deaconu M, Lejay A, Meira JA, Nichil G , State R,"A recommendation system for car insurance",European Actuarial Journal volume 10, 2020

Lephoto A, Kogeda O.P. ,Modelling a Rule Based System for Medical Underwriting in an Insurance Industry,Proceedings of the World Congress on Engineering and Computer Science 2014 Vol I

Lertpunyavuttikul, P., Chuenprasertsuk, P., & Glomglome, S. (2017, November). Usage-based Insurance Using IoT Platform. In 2017 21st International Computer Science and Engineering Conference (ICSEC) (pp. 1-5). IEEE.

WCECS 2014, 22-24 October, 2014, San Francisco, USA

Lin, W., Alvarez, S. A., & Ruiz, C. (2000). Collaborative recommendation via adaptive association rule mining. Data Mining and Knowledge Discovery, 6(1), 83-105.

Lindholm, A., Wahlström, N., Lindsten, F., & Schön, T. B. (2019). Supervised Machine Learning, Lecture notes for the Statistical Machine Learning course. Department of Information Technology, Uppsala University.

Lukas S.Stefani D,Widjaja P. (2019)†Comparing SVM and GLM in calculating insurance premium for flight delayâ€, Pervasive Health: Pervasive Computing Technologies for Healthcare. https://doi.org/10.1145/3369114.3369160

Marsh K., Fayek A. R., “SuretyAssist: Fuzzy Expert System to Assist Surety Underwriters in Evaluating Construction Contractors for Bondingâ€, †Journal Of Construction Engineering And Managementâ€, November 2010

Merritt, D. (2012). Building expert systems in prolog Springer Science & Business Media.

Macedo, L.: The role of the underwriter in insurance. Primer Series on Insurance, Washington: The World Bank, iss. 8, (2009).

Maehashi, K., & Shintani, M. (2020). Macroeconomic forecasting using factor models and machine learning: an application to Japan. Journal of the Japanese and International Economies, 58 (March), Article 101104. 10.1016/j.jjie.2020.101104 .

Maier, M., Carlotto, H., Saperstein, S., Sanchez, F., Balogun, S., & Merritt, S. (2020). Improving the accuracy and transparency of underwriting with AI to transform the life insurance industry. AI Magazine, 41(3), 78-93.

McGlade, D., & Scott-Hayward, S. (2019). ML-based cyber incident detection for electronic medical record (EMR) systems. Smart Health, 12 , 3–23. 10.1016/j.smhl.2018.05.001 . Mita, Y., Inose, R., Goto, R., Kusama, Y., Koizumi, R., Yamasaki, D., …, & Mu- raki, Y. (2021). An alternative index for evaluating AMU and anti-methicillin-resistant Staphylococcus aureus agent use: A study based on the national database of health insurance claims and specific health checkups data of Japan. Journal of Infection and Chemotherapy , (xxxx). 10.1016/j.jiac.2021.02.009 .

Mita, Y., Inose, R., Goto, R., Kusama, Y., Koizumi, R., Yamasaki, D., …, & Mu- raki, Y. (2021). An alternative index for evaluating AMU and anti-methicillin-resistant Staphylococcus aureus agent use: A study based on the national database of health insurance claims and specific health checkups data of Japan. Journal of Infection and Chemotherapy , (xxxx). 10.1016/j.jiac.2021.02.009 .

Mustika W,Murfi H,Widyaningsih Y (2019. Analysis Accuracy of XGBoost Model for Multiclass Classification - A Case Study of Applicant Level Risk Prediction for Life Insurance. Proceeding - 2019 5th International Conference on Science in Information Technology. https://doi.org/10.1109/ICSITech46713.2019.8987474

Müller, D., & Te, Y. F. (2017, December). Insurance premium optimization using motor insurance policies—A business growth classification approach. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4154-4158). IEEE.

Mohammed, M., Khan, M. B., & Bashier, E. B. (2017). Machine Learning Algorithms and Applications. Taylor & Francis Group.

Neota Logic. (2016). Artificial intelligence in law: The state of play 2016, part 1. Retrieved from https://www.neotalogic.com/2016/02/28/artificial-intelligence-in-law-the-state-of-play-2016-part-1/

Newman, S., (2015). Building microservices: designing fine-grained systems. O'Reilly Media, Inc.

Nuruzzaman M., Hussain O. K., “IntelliBot: A Dialogue-based chatbot for the insurance industryâ€, †Knowledge-Based Systemsâ€, 2020

Pham T.M.P., Hoang T.M.H., "Maritime cargo claims in Vietnam: practical issues and the design of a virtual consultancy expert system based on artificial intelligience to assist non-lawyer users","World Maritime University Dissertations",11-4-2018

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302

Peterson, D. (2017). Maximize Efficiency: How Automation Can Improve Your Loan Origination Process. Moody's Analytics.

Poola, I. (2017). How Artificial Intelligence in Impacting Real Life Every day. International Journal of Advance Research and Development., 2(100), 96-100.

Pressman, Roger, S.. Rekayasa Perangkat Lunak.Pendekatan Praktisi. Edisi 7. Yogyakarta : Andi, 2012

Provost, F., & Fawcett, T. (2013). Data Science for Business. CA 95472: O'Reilly.

Rahma, AS, Analisis dan Pengembangan Sistem Pendukung Keputusan Underwriting Asuransi Lini Bisnis Marine Cargo berbasis Case Based Reasoning. Tesis Sekolah Pascasarjana Institut Pertanian Bogor, 2017

Ramnathan, P. P., Revathy, S., & Mani, V. (2020, May). Smart Property Insurance using IoT. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 796-800). IEEE.

Richardson, C. (2018). Microservices patterns. Manning Publications Company.

Ricci F, Rokach L,Shapira B,"Recommender Systems Handbook", Springer Science+Business Media, New Yorks, USA, 2011

Riikkinen M, Saarijarvi H, Sarlin P, Lahteenmaki I, “Using artificial intelligence to create value in insuranceâ€, â€International Conference on Computing, International Journal of Bank Marketingâ€,, 2018

Reinfokus. Media Informasi Asuransi dan Reasuransi.Oktober 2019

Sakellaridou S. 2009. Maritime Insurance & Piracy. Call for Papers for the AIDA Europe Conference. Zurich (CH): AIDA.

Salatin, P., Yadollahi, F. & Eslambolchi, S., 2014. The Effect Of ICT On Insurance Industry In Selected Countries. Research Journal Of Economics, Business And ICT , 9(1).

Sachan S., Yang J., Dong-LingXu, Benavides D.E.,Li Y, "An explainable AI decision-support-system to automate loan underwriting","Expert Systems with Applications",Volume 144, 15 April 2020.

Santoso C.B., Prabowo H, Warnars H.L.H.S., Fajar A.N., 2021,"Smart Insurance System Model Concept for Marine Cargo Business",2021 International Conference on Data Science and Its Applications (ICoDSA)

Sauro, J., & Lewis, J. R. (2016). Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann.

Sekaran U, Bougie R, "Research Methode for Business",Wiley,2013

Singh D., Kumar P.,"Design of Integrated Business Intelligence System Framework for Insurance Business Processesâ€,"International Journal of Applied Information Systems",Volume 3 No3 July 2012

Singh, P. K., Singh, R., Muchahary, G., Lahon, M., & Nandi, S. (2019, October). A blockchain-based approach for usage based insurance and incentive in its. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1202-1207). IEEE.

Sukono, Riaman,Lesmana E, et al, 2018, †Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian methodâ€, IOP Conference Series: Materials Science and Engineering, https://doi.org/10.1088/1757-899X/300/1/012027

Surat Edaran Otoritas Jasa Keuangan Nomor 6 /SEOJK.05/2017

SU, X. & Khoshgoftaar, T.M. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 4.

Tang, J., HU, X. & Liu, H. 2013. Social recommendation: a review. Social Network Analysis and Mining, 3(4), 1113-1133.

Teruel M.A., Navarro E., and Jaquero V.L., “Comparing Goal-Oriented Approaches to Model Requirements for CSCW,†Evaluation of Novel Approaches to Software Engineering, pp. 169–184, 2012.

Undang-Undang No. 2 Th 1992 tentang Usaha Perasuransian

Undang-Undang No. 17 Th 2008 tentang Pelayaran

UNCTAD] United Nations Conference on Trade and Development. 1982. Legal and Documentary Aspects of the Marine Insurance Contract [Organizational Report]. New York (US): UN.

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