Aktüeryal Karar Destek Süreçlerinde Veri Analitiğinin Rolü
Özet
Bu çalışma, büyük veri analitiğinin sigorta ve aktüerya alanındaki rolünü incelemektedir. Büyük veri; hacim, hız, çeşitlilik, doğruluk ve değer boyutlarıyla sigorta şirketleri için stratejik bir kaynak niteliğindedir. Çalışmada öncelikle büyük verinin tanımı, özellikleri ve sigortacılıkta kullanılan veri türleri ele alınmış; ardından problem tanımlama, veri toplama, hazırlama, modelleme, hesaplama, test etme ve raporlama aşamalarını içeren veri analitiği süreci açıklanmıştır. Analitik yaklaşımlar; tanımlayıcı, teşhis edici, tahmin edici, eylem geliştirici ve bilişsel analitik olmak üzere beş başlıkta sınıflandırılmıştır. Literatür incelemesi, büyük veri analitiğinin fiyatlandırma, sahtekârlık tespiti, çevresel riskler, portföy optimizasyonu, müşteri segmentasyonu ve regülasyon/etik tartışmalarındaki katkılarını ortaya koymaktadır. Büyük veri, daha doğru risk değerlendirmesi, kişiselleştirilmiş fiyatlandırma ve operasyonel verimlilik sağlamakta; aynı zamanda bilgi asimetrisini azaltarak sigorta sisteminin daha adil ve sürdürülebilir işlemesine katkı sunmaktadır. Ancak, gizlilik ihlalleri, etik kaygılar, regülasyonlarla uyum zorlukları ve yüksek teknolojik maliyetler gibi sınırlılıklar da söz konusudur. Sonuç olarak, büyük veri analitiğinin sigorta sektöründe sunduğu faydaların sürdürülebilir biçimde hayata geçirilebilmesi, şeffaf, güvenilir ve insan odaklı uygulamalarla mümkündür.
Referanslar
Allianz (2025). Allianz Global Insurance Report 2025: Rising demand for protection. (20/05/2025 tarihinde https://www.allianz.com/en/economic_research/insights/publications/specials_fmo/250527-global-insurance-report.html adresinden ulaşılmıştır).
Al-Omoush, K.S., Garcia-Monleon, F. & Iglesias, J.M.M., (2024). Exploring the interaction between big data analytics, frugal innovation, and competitive agility: The mediating role of organizational learning. Technological Forecasting and Social Change 200, 3, 123188.
Arora, Y. & Goyal, D. (2016) Big data: A review of analytics methods & techniques. In Proceedings of the 2nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, 14–17 December 2016; pp. 225–230.
Arrigo E., Liberati C. & Paolo M. (2021). Social media data and users’ preferences: A statistical analysis to support marketing communication. Big Data Research 24, , 100189.
Atitallah, S., Driss, M., Boulila, W., et al. (2020). Leveraging deep learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review 38, 4, 100303.
Calude, C. & Longo, G. (2017). The deluge of spurious correlations in big data. Foundations of Science 22, 3, 595–612.
Cevolini, A. & Esposito E. (2022). From Actuarial to Behavioural Valuation. The impact of telematics onmotor insurance. Valuatıon Studıes, 9(1), 109-139.
Chan, I. W., Tseung, S. C., Badescu, A. L., et al. (2024). Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behavior. North American Actuarial Journal, 29(2), 275–309. Doi: 10.1080/10920277.2024.2376816
Chandarana P. & Vijayalakshrni, M. (2014) Big Data Analytics Framework, International Conference on Circuits, System, Communication and Information Technology Applications, IEEE,2014 4-5 April 2014 in Mumbai, India. (pp. 430-434)
Charpentier, A. & Vamparys, X. (2025). Artificial intelligence and personalization of insurance: Failure or delayed ignition? Big Data & Society, 12(1). Doi: 10.1177/20539517241291817
Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy ve Leadership. 42 (4): 45–50.
Deepa, N., Pham, Q., Nguyen, D.C., et al. (2022). A survey on blockchain for big data: Approaches, opportunities, and future directions. Future Generation Computer Systems 131, 6 , 209–226.
Dey A. K., Lyubchich V. & Gel Y. R. (2021). Modeling Weather-induced Home insurance risks with support vector machine regression. ArXiv Preprint ArXiv:2103.08761.
Diouf, P.S., Boly, A. & Ndiaye, S. (2018). Variety of data in the ETL processes in the cloud: Stateof- the-art. International Conference on Innovative Research and Development (ICIRD). IEEE, 1–5.
Duan, L. & Da Xu, L. (2024). Data Analytics in Industry 4.0: A Survey. Inf Syst Front 26, 2287–2303. Doi: 10.1007/s10796-021-10190-0
EIOPA (2019) European Insurance and Occupational Pensions Authority . Big Data Analytics in Motor and Health Insurance. (30/04/2025 tarihinde https://www.eiopa.europa.eu/publications/big-data-analytics-motor-and-health-insurance_en adresinden ulaşılmıştır)
Eling, M., Gemmo, I. & Guxha. D. (2024). Big Data, Risk Classification, and Privacy in Insurance Markets.” Geneva Risk Insurance Review 49:75–126.
Farbiz, F., Miaolong, Y. & Yu. Z. (2020). A cognitive analytics based approach for machine health monitoring, anomaly detection, and predictive maintenance. In Proceedings of the 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA’20). IEEE, 1104–1109.
Fedushko, S., Ustyianovych, T. & Gregus. M., (2020). Real-time high-load infrastructure transaction status output prediction using operational intelligence and big data technologies. Electronics 9, 4 (2020), 668.
Frees, E.W. (1990). Stochastic Life Contingencies with Solvency Considerations. Trans. Soc. Actuar. 42, 91–148.
Garg, P. & Sharma, V. (2014). An efficient and secure data storage in mobile cloud computing through RSA and hash function. In Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT’14). IEEE, 334–339.
Hegde P. & Maddikunta, P. (2023). Amalgamation of blockchain with resource-constrained IoT devices for healthcare applications–state of art, challenges and future directions. International Journal of Cognitive Computing in Engineering 4, 1, 220–239.
Henckaerts, R., Côté, M.P., & Antonio, K., (2020) Boosting insights in insurance tariff plans with tree-based machine learning methods. North Am. Actuarial J. 25(2), 1–31.
IAIS (2020) Issues Paper on the Use of Big Data Analytics in Insurance (14/06/2025 tarihinde 200319-Issues-Paper-on-Use-of-Big-Data-Analytics-in-Insurance-FINAL.pdf adresinden ulaşılmıştır).
Jaiswal, R., Gupta, S., Tiwari, A.K., et al. (2024b), Big data and machine learning-based decision support system to reshape the vaticination of insurance claims, Technological Forecasting and Social Change, Vol. 209, p. 123829, doi: 10.1016/j.techfore.2024.123829.
Jeble, S. & Patil, Y. (2016). Role of big data and predictive analytics. International Journal of Automation and Logistics, 2(4), 307-331.
Kaur, H. & Phutela, A. (2018). Commentary upon descriptive data analytics. In Proceedings of the 2018 2nd International Conference on Inventive Systems and Control (ICISC’18). IEEE, 678–683.
Kaya, H. (2024). Riskified Fraud Detection Using Machine Learning: Insurance Claims. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, 5 ( 1 ), 39–56.
Keller, B., Eling, M. & Schmeiser, H. (2018) Big data and insurance: Implications for innovation, competition and privacy. Geneva Association-International Association for the Study of Insurance Talstrasse 70, CH-8001 Zurich
Keskar, V., Yadav, J., & Kumar, A. H. (2020). 5V’s of Big Data Attributes and their Relevance and Importance across Domains. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, 9 (11), 2278-3075.
Lutfi, A., Alrawad, M. , Alsyouf, A., et al. (2023). Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling. Journal of Retailing and Consumer Services 70, 1, 103129.
Li, X. (2023). Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models. Academic Journal of Computing & Information Science 6, no. 4: 118–125
Mullins, M., Holland, C. P. & Cunneen, M. (2021). Creating ethics guidelines for artificial intelligence and big data analytics customers: The case of the con-sumer European insurance market. Patterns, 2(10), 100362.
Naganathan, V. (2018). Comparative analysis of Big data, Big data analytics: Challenges and trends. International Research Journal of Engineering and Technology (IRJET), 5(05), 1948-1964
Neale, F.R., Drake, P.P., Jin, L. et al. (2025).Technology investment and insurer efficiency. Geneva Pap Risk Insur Issues Pract 50, 8–33.
OECD. (2020). The Impact of Big Data and Artificial Intelligence (AI) in the Insurance Sector. (20/05/2025 tarihinde https://www.oecd.org/finance/The-Impact-Big-Data-AI-Insurance-Sector.pdf adresinden ulaşılmıştır).
Psychoula, I., Gutmann, A., Mainali, P., et al. (2021). Explainable Machine Learning for Fraud Detection. Computer , 54, 49–59.
Raghupathi, W. & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2, 1 (2014), 1–10.
Saba, B. & Srivastava, D. (2014) "Data Quality : The other face of Big Data", in IEEE, 2014.
Sauce, M., Chancel, A. & Ly, A. (2023). Ai and ethics in insurance: a new solution to mitigate proxy discrimination in risk modeling, ArXiv, vol. abs/2307.13616.
Singh, T., Kalra, R., Mishra, S., et al. (2022). An efficient real-time stock prediction exploiting incremental learning and deep learning. Evolving Systems 14, 6 , 1–19.
Singh, T., Rajput, V., Prasad, U., et al. (2023). Real-time traffic light violations using distributed streaming. The Journal of Supercomputing 79, 7, 7533–7559.
So, B., Boucher, J.-P. & Valdez, E.A. (2021). Cost-sensitive multi-class Adaboost for understanding driving behavior based on telematics. ASTIN Bulletin: J. of IAA.; 51:719-751
Tarr, A.A., Tarr, J-A. & Peña A. (2023) On-Demand Insurance. Tarr, A.A., Tarr, J-A., Thompson, M., Wilkinson, D. (Ed.), The Global Insurance Market and Change (19)., Londra, ImprintInforma Law from Routledge
The Geneva Association (2017). Big Data and Insurance: Implications for Innovation, Competition and Privacy (22/04/2025 tarihinde https://www.genevaassociation.org/sites/default/files/research-topics-document-type/pdf_public/research_brief_-_big_data_and_insurance.pdf adresinden ulaşılmıştır).
TSB (2024). 2024 Yılı Aralık Sonu Istatistikleri. (20/05/2025 tarihinde https://tsb.org.tr/tr/AnasayfaSlider/116 adresinden ulaşılmıştır).
van den Boom, F. (2021). Regulating Telematics Insurance. Marano, P., Noussia, K. (Ed.) Insurance Distribution Directive: A Legal Analysis AIDA Europe Research Series on Insurance Law and Regulation, vol 3. Springer, Doi: 10.1007/978-3-030-52738-9_12.
Villars, R. L., Olofson, C. W. & Eastwood, M. (2011). Big data: What it is and why you should care. IDC White Paper. Framingham, MA: IDC.
Wang, C., Wang, Y., Ye, Z., et al. (2018). Credit card fraud detection based on whale algorithm optimized BP neural network. In Proceedings of the 2018 13th International Conference on Computer Science and Education (ICCSE’18). IEEE, 1–4.
Referanslar
Allianz (2025). Allianz Global Insurance Report 2025: Rising demand for protection. (20/05/2025 tarihinde https://www.allianz.com/en/economic_research/insights/publications/specials_fmo/250527-global-insurance-report.html adresinden ulaşılmıştır).
Al-Omoush, K.S., Garcia-Monleon, F. & Iglesias, J.M.M., (2024). Exploring the interaction between big data analytics, frugal innovation, and competitive agility: The mediating role of organizational learning. Technological Forecasting and Social Change 200, 3, 123188.
Arora, Y. & Goyal, D. (2016) Big data: A review of analytics methods & techniques. In Proceedings of the 2nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, 14–17 December 2016; pp. 225–230.
Arrigo E., Liberati C. & Paolo M. (2021). Social media data and users’ preferences: A statistical analysis to support marketing communication. Big Data Research 24, , 100189.
Atitallah, S., Driss, M., Boulila, W., et al. (2020). Leveraging deep learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review 38, 4, 100303.
Calude, C. & Longo, G. (2017). The deluge of spurious correlations in big data. Foundations of Science 22, 3, 595–612.
Cevolini, A. & Esposito E. (2022). From Actuarial to Behavioural Valuation. The impact of telematics onmotor insurance. Valuatıon Studıes, 9(1), 109-139.
Chan, I. W., Tseung, S. C., Badescu, A. L., et al. (2024). Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behavior. North American Actuarial Journal, 29(2), 275–309. Doi: 10.1080/10920277.2024.2376816
Chandarana P. & Vijayalakshrni, M. (2014) Big Data Analytics Framework, International Conference on Circuits, System, Communication and Information Technology Applications, IEEE,2014 4-5 April 2014 in Mumbai, India. (pp. 430-434)
Charpentier, A. & Vamparys, X. (2025). Artificial intelligence and personalization of insurance: Failure or delayed ignition? Big Data & Society, 12(1). Doi: 10.1177/20539517241291817
Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy ve Leadership. 42 (4): 45–50.
Deepa, N., Pham, Q., Nguyen, D.C., et al. (2022). A survey on blockchain for big data: Approaches, opportunities, and future directions. Future Generation Computer Systems 131, 6 , 209–226.
Dey A. K., Lyubchich V. & Gel Y. R. (2021). Modeling Weather-induced Home insurance risks with support vector machine regression. ArXiv Preprint ArXiv:2103.08761.
Diouf, P.S., Boly, A. & Ndiaye, S. (2018). Variety of data in the ETL processes in the cloud: Stateof- the-art. International Conference on Innovative Research and Development (ICIRD). IEEE, 1–5.
Duan, L. & Da Xu, L. (2024). Data Analytics in Industry 4.0: A Survey. Inf Syst Front 26, 2287–2303. Doi: 10.1007/s10796-021-10190-0
EIOPA (2019) European Insurance and Occupational Pensions Authority . Big Data Analytics in Motor and Health Insurance. (30/04/2025 tarihinde https://www.eiopa.europa.eu/publications/big-data-analytics-motor-and-health-insurance_en adresinden ulaşılmıştır)
Eling, M., Gemmo, I. & Guxha. D. (2024). Big Data, Risk Classification, and Privacy in Insurance Markets.” Geneva Risk Insurance Review 49:75–126.
Farbiz, F., Miaolong, Y. & Yu. Z. (2020). A cognitive analytics based approach for machine health monitoring, anomaly detection, and predictive maintenance. In Proceedings of the 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA’20). IEEE, 1104–1109.
Fedushko, S., Ustyianovych, T. & Gregus. M., (2020). Real-time high-load infrastructure transaction status output prediction using operational intelligence and big data technologies. Electronics 9, 4 (2020), 668.
Frees, E.W. (1990). Stochastic Life Contingencies with Solvency Considerations. Trans. Soc. Actuar. 42, 91–148.
Garg, P. & Sharma, V. (2014). An efficient and secure data storage in mobile cloud computing through RSA and hash function. In Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT’14). IEEE, 334–339.
Hegde P. & Maddikunta, P. (2023). Amalgamation of blockchain with resource-constrained IoT devices for healthcare applications–state of art, challenges and future directions. International Journal of Cognitive Computing in Engineering 4, 1, 220–239.
Henckaerts, R., Côté, M.P., & Antonio, K., (2020) Boosting insights in insurance tariff plans with tree-based machine learning methods. North Am. Actuarial J. 25(2), 1–31.
IAIS (2020) Issues Paper on the Use of Big Data Analytics in Insurance (14/06/2025 tarihinde 200319-Issues-Paper-on-Use-of-Big-Data-Analytics-in-Insurance-FINAL.pdf adresinden ulaşılmıştır).
Jaiswal, R., Gupta, S., Tiwari, A.K., et al. (2024b), Big data and machine learning-based decision support system to reshape the vaticination of insurance claims, Technological Forecasting and Social Change, Vol. 209, p. 123829, doi: 10.1016/j.techfore.2024.123829.
Jeble, S. & Patil, Y. (2016). Role of big data and predictive analytics. International Journal of Automation and Logistics, 2(4), 307-331.
Kaur, H. & Phutela, A. (2018). Commentary upon descriptive data analytics. In Proceedings of the 2018 2nd International Conference on Inventive Systems and Control (ICISC’18). IEEE, 678–683.
Kaya, H. (2024). Riskified Fraud Detection Using Machine Learning: Insurance Claims. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, 5 ( 1 ), 39–56.
Keller, B., Eling, M. & Schmeiser, H. (2018) Big data and insurance: Implications for innovation, competition and privacy. Geneva Association-International Association for the Study of Insurance Talstrasse 70, CH-8001 Zurich
Keskar, V., Yadav, J., & Kumar, A. H. (2020). 5V’s of Big Data Attributes and their Relevance and Importance across Domains. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, 9 (11), 2278-3075.
Lutfi, A., Alrawad, M. , Alsyouf, A., et al. (2023). Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling. Journal of Retailing and Consumer Services 70, 1, 103129.
Li, X. (2023). Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models. Academic Journal of Computing & Information Science 6, no. 4: 118–125
Mullins, M., Holland, C. P. & Cunneen, M. (2021). Creating ethics guidelines for artificial intelligence and big data analytics customers: The case of the con-sumer European insurance market. Patterns, 2(10), 100362.
Naganathan, V. (2018). Comparative analysis of Big data, Big data analytics: Challenges and trends. International Research Journal of Engineering and Technology (IRJET), 5(05), 1948-1964
Neale, F.R., Drake, P.P., Jin, L. et al. (2025).Technology investment and insurer efficiency. Geneva Pap Risk Insur Issues Pract 50, 8–33.
OECD. (2020). The Impact of Big Data and Artificial Intelligence (AI) in the Insurance Sector. (20/05/2025 tarihinde https://www.oecd.org/finance/The-Impact-Big-Data-AI-Insurance-Sector.pdf adresinden ulaşılmıştır).
Psychoula, I., Gutmann, A., Mainali, P., et al. (2021). Explainable Machine Learning for Fraud Detection. Computer , 54, 49–59.
Raghupathi, W. & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2, 1 (2014), 1–10.
Saba, B. & Srivastava, D. (2014) "Data Quality : The other face of Big Data", in IEEE, 2014.
Sauce, M., Chancel, A. & Ly, A. (2023). Ai and ethics in insurance: a new solution to mitigate proxy discrimination in risk modeling, ArXiv, vol. abs/2307.13616.
Singh, T., Kalra, R., Mishra, S., et al. (2022). An efficient real-time stock prediction exploiting incremental learning and deep learning. Evolving Systems 14, 6 , 1–19.
Singh, T., Rajput, V., Prasad, U., et al. (2023). Real-time traffic light violations using distributed streaming. The Journal of Supercomputing 79, 7, 7533–7559.
So, B., Boucher, J.-P. & Valdez, E.A. (2021). Cost-sensitive multi-class Adaboost for understanding driving behavior based on telematics. ASTIN Bulletin: J. of IAA.; 51:719-751
Tarr, A.A., Tarr, J-A. & Peña A. (2023) On-Demand Insurance. Tarr, A.A., Tarr, J-A., Thompson, M., Wilkinson, D. (Ed.), The Global Insurance Market and Change (19)., Londra, ImprintInforma Law from Routledge
The Geneva Association (2017). Big Data and Insurance: Implications for Innovation, Competition and Privacy (22/04/2025 tarihinde https://www.genevaassociation.org/sites/default/files/research-topics-document-type/pdf_public/research_brief_-_big_data_and_insurance.pdf adresinden ulaşılmıştır).
TSB (2024). 2024 Yılı Aralık Sonu Istatistikleri. (20/05/2025 tarihinde https://tsb.org.tr/tr/AnasayfaSlider/116 adresinden ulaşılmıştır).
van den Boom, F. (2021). Regulating Telematics Insurance. Marano, P., Noussia, K. (Ed.) Insurance Distribution Directive: A Legal Analysis AIDA Europe Research Series on Insurance Law and Regulation, vol 3. Springer, Doi: 10.1007/978-3-030-52738-9_12.
Villars, R. L., Olofson, C. W. & Eastwood, M. (2011). Big data: What it is and why you should care. IDC White Paper. Framingham, MA: IDC.
Wang, C., Wang, Y., Ye, Z., et al. (2018). Credit card fraud detection based on whale algorithm optimized BP neural network. In Proceedings of the 2018 13th International Conference on Computer Science and Education (ICCSE’18). IEEE, 1–4.
