Doğuş Üniversitesi Dergisi 1308-6979 Dogus University 10.31671/doujournal.1538261 Finance Finans SİGORTA SAHTECİLİĞİ ARAŞTIRMALARININ BİBLİYOMETRİK ANALİZİ: GENEL GÖRÜNÜM VE EĞİLİMLER A BIBLIOMETRIC ANALYSIS OF INSURANCE FRAUD RESEARCH: OVERVIEW AND TRENDS https://orcid.org/0000-0003-2498-2988 Ersoy Behlül BILECIK SEYH EDEBALI UNIVERSITY, BOZÜYÜK VOCATIONAL SCHOOL 01 24 2025 26 1 339 358 08 24 2024 09 15 2024 Copyright © 2000, Dogus University Journal 2000 Dogus University Journal

Sigorta sahteciliği sigorta sektörünün uzun yıllar mücadele ettiği en temel sorunların başında gelmektedir. Sahtecilik eylemeleri sonucu ortaya çıkan maliyet, sigorta şirketlerinin bilançolarını bozmakta ve karlılıklarını düşürmektedir. Bununla birlikte sigorta sahteciliklerinin sektörün teminat kapasitesi üzerinde de daraltıcı etkisi bulunmaktadır. Deprem, sel gibi doğal afet kaynaklı katastrofik risklerin yönetimi sahteciliğin getirdiği maliyetlerden dolayı zorlaşmaktadır. Sigorta sahteciliğinin ekonomiye ve sektöre etkisinin önemi düzeyinde konu, araştırmacıların da ilgisini çekmektedir. Bu noktada çalışmanın amacı, sigorta sahteciliği alanındaki uluslararası araştırmaların kapsamlı bir değerlendirmesini yaparak, literatürün mevcut durumunu ve gelişim alanlarını ortaya koymaktır. Bu kapsamda R yazılımı Bibliometrix kütüphanesi altyapısıyla kullanılan Biblioshiny yardımıyla sigorta sahteciliğine ilişkin literatürün bibliyometrik analizi yapılmıştır. Çalışmada sigorta sahteciliğini konu alan ve Scopus veri tabanında taranan 2007-2024 yılları arasındaki 586 çalışma incelenmiştir. Buna göre çalışmada sırasıyla ülke, yazar, çalışma, kaynak, tema ve anahtar odaklı performans değerlendirmesine yönelik bulgular paylaşılmıştır. Çalışmanın sonuçlarına göre, sigorta sahteciliğine araştırmacıların ilgisi büyüktür. Son yıllardaki çalışma sayılarındaki dramatik artış dikkate alındığında bu ilginin önümüzdeki yıllarda da devam edeceği tahmin edilmektedir. Literatürün ağırlıkla otomobil ve sağlık sigortacılığı branşlarındaki sahtecilikle mücadele yöntemlerine odaklandığı görülmektedir. Bununla birlikte sahtecilikle mücadelede davranışsal nedenleri araştıran çalışmaların sınırlı sayıda ve kapsamda olduğu da tespit edilmiştir. Son olarak sigortacılık sisteminin mevcut homojen ve uluslararası standartları, çalışmaların etkisini artıran unsurlara da yansımıştır. Bu noktada, literatürde ağırlık merkezi olan çalışmaların yazarları büyük oranda uluslararası iş birliği ağlarının içinde yer almaktadır.

Insurance fraud is one of the most significant issues the insurance industry has been struggling with for many years. The financial costs resulting from fraudulent activities disrupt the financial statements of insurance companies and reduce their profitability. Moreover, insurance fraud has a constraining effect on the industry's capacity to provide coverage. Managing catastrophic risks, such as those stemming from natural disasters like earthquakes and floods, becomes more challenging due to the costs imposed by fraud. The impact of insurance fraud on the economy and the sector has attracted the attention of researchers, given its importance. This study aims to provide a comprehensive evaluation of international research in insurance fraud, revealing the current state of the literature and identifying areas for further development. In this context, a bibliometric analysis of the literature on insurance fraud was conducted using the Biblioshiny tool within the Bibliometrix package in R software. The study examined 586 works on insurance fraud published in the Scopus database between 2007 and 2024. The study's findings are shared regarding performance evaluation focused on country, author, work, source, theme, and keywords. According to the results, researchers have shown a significant interest in insurance fraud. Considering the dramatic increase in the number of studies in recent years, this interest is expected to continue in the coming years. The literature predominantly focuses on methods to combat fraud in the automobile and health insurance sectors. However, it has also been observed that there are only a limited number of studies investigating the behavioral reasons behind fraud. Finally, the existing homogeneous and international standards of the insurance system have also been reflected in the factors that increase the impact of the studies. At this point, the studies' authors are central to the literature and essentially participate in international collaboration networks.

 Sigorta Sahteciliği Sigorta Suistimali Sigortacılık Bibliyometrik Analiz Biblioshiny Insurance Fraud Insurance Abuse Insurance Bibliometric Analysis Biblioshiny Akbulut, H. (2023). Osmanlı’da sigorta sektörünün gelişimi ve yapısal problemleri. H. Meral (Ed.), 21. Yüzyılda Türk Sigorta Sektörüne 21 Tavsiye içinde (9-20 ss.) Ankara: Nobel Bilimsel Eserler. Al-Hashedi, K. G., ve Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 100402. https://doi.org/10.1016/j.cosrev.2021.100402 Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., ve Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62, 101744. https://doi.org/10.1016/j.ribaf.2022.101744 Baesens, B., Van Vlasselaer, V., ve Verbeke, W. (2015). Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection. John Wiley & Sons. https://doi.org/10.1002/9781119146841 Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century-A review. Journal of Informetrics, 2(1), 1-52. https://doi.org/10.1016/j.joi.2007.11.001 Bhattacharyya, D. K., ve Kalita, J. K. (2013). Network anomaly detection: A machine learning perspective. Chapman and Hall/CRC. https://doi.org/10.1201/b15088 Biju, A. K. V. N., Thomas, A. S., ve Thasneem, J. (2024). Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis. Quality & Quantity, 58(1), 849-878. https://doi.org/10.1007/s11135-023-01673-0 Brockett, P. L., Derrig, R. A., Golden, L. L., Levine, A., ve Alpert, M. (2002). Fraud classification using principal component analysis of RIDITs. Journal of Risk and Insurance, 69(3), 341-371. https://doi.org/10.1111/1539- 6975.00027 CAIF (Coalition Against Insurance Fraud) (2022). The Impact of Insurance Fraud on the U.S. Economy. Erişim adresi https://insurancefraud.org/wp- content/uploads/The-Impact-of-Insurance-Fraud-on-the-U.S.-Economy- Report-2022-8.26.2022.pdf Chauhan, V., ve Yadav, J. (2024). Bibliometric review of telematics-based automobile insurance: Mapping the landscape of research and knowledge. Accident Analysis & Prevention, 196, 107428. https://doi.org/10.1016/j.aap.2023.107428 Çelik, M., Elmas, B. ve Korkmaz, E. (2024). Dijital finans araştırmalarının bilim haritalama teknikleri ile bibliyometrik analizi. Muhasebe ve Finansman Dergisi, (103), 113-134. https://doi.org/10.25095/mufad.1457529 Debener, J., Heinke, V., ve Kriebel, J. (2023). Detecting insurance fraud using supervised and unsupervised machine learning. Journal of Risk and Insurance, 90(3), 743-768. https://doi.org/10.1111/jori.12427 Dehghanpour, A., ve Rezvani, Z. (2015). The profile of unethical insurance customers: A European perspective. International Journal of Bank Marketing, 33(3), 298-315. https://doi.org/10.1108/IJBM-12- 2013-0143 Eletter, S. F. (2024). The use of blockchain in the insurance industry: A bibliometric analysis. Insurance Markets and Companies, 15(1), 12-29. https://doi.org/10.21511/ins.15(1).2024.02 Ersoy, B. (2018). Sigortacılığın tarihsel gelişimi. F. Akın (Ed.), Sigortacılığa Giriş içinde (54-69 ss.). Bursa: Ekin Yayınları. Gomes, C., Jin, Z., ve Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88(3), 591-624. https://doi.org/10.1111/jori.12359 Hassan, A. K. I., ve Abraham, A. (2016). Modeling insurance fraud detection using imbalanced data classification. Advances in Nature and Biologically Inspired Computing: Proceedings of the 7th World Congress on Nature and Biologically Inspired Computing (NaBIC2015) in Pietermaritzburg, South Africa, held December 01-03, 2015 içinde (117-127 ss.) Springer International Publishing. https://doi.org/10.1007/978-3-319- 27400-3_11 Hilal, W., Gadsden, S. A., ve Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems With Applications, 193,116429. https://doi.org/10.1016/j.eswa.2021.116429 Jackson, G. (1971). Marine insurance frauds in Scotland 1751–1821: Cases of deliberate shipwreck tried in the Scottish court of admiralty. The Mariner's Mirror, 57(3), 307-322. https://doi.org/10.1080/00253359.1971.10658608 Kerim, D. E., ve Cula, S. (2023). Türkiye’de sigorta suistimal problemleri ve çözüm önerileri. Başkent Üniversitesi Ticari Bilimler Fakültesi Dergisi, 7(2), 145- 166. Erişim adresi https://dergipark.org.tr/tr/pub/jcsci/issue/80245/1344954 Khudzari, J. M., Kurian, J., Tartakovsky, B., ve Raghavan, G. V. (2018). Bibliometric analysis of global research trends on microbial fuel cells using Scopus database. Biochemical Engineering Journal, 136, 51-60. https://doi.org/10.1016/j.bej.2018.05.002 Lammers, F., & Schiller, J. (2010). Contract design and insurance fraud: an experimental investigation, FZID Discussion Paper No: 19-2010. Leal, S., Vrij, A., Warmelink, L., Vernham, Z., ve Fisher, R. P. (2015). You cannot hide your telephone lies: Providing a model statement as an aid to detect deception in insurance telephone calls. Legal and Criminological Psychology, 20(1), 129-146. https://doi.org/10.1111/lcrp.12017 Meral, H., Ersoy, B., ve Dilek, I. (2024). Insights into earthquake insurance demand in high-risk regions: A case study of Turkey. International Journal of Disaster Risk Reduction, 104725. https://doi.org/10.1016/j.ijdrr.2024.104725 Mirsky, Y., Mahler, T., Shelef, I., ve Elovici, Y. (2019). {CT-GAN}: Malicious tampering of 3d medical imagery using deep learning. 28th USENIX Security Symposium (USENIX Security 19) içinde, (461-478 ss.) Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., ve Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información/Information Professional, 29(1). https://doi.org/10.3145/epi.2020.ene.03 Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system Bitcoin: A Peer- to-Peer Electronic Cash System. Erişim adresi https://bitcoin. org/en/bitcoin-paper Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., ve Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569. https://doi.org/10.1016/j.dss.2010.08.006 Picard, P. (2000). Economic analysis of insurance fraud. Dionne, G. (Ed.), Handbook of Insurance. Huebner International Series on Risk, Insurance, and Economic Security içinde (315-362 ss.) Dordrecht: Springer. Qadri, H. M. U., Ali, H., Jafar, A., Tahir, A. U. M. ve Abbasi, M. A. (2024). Exploring the hot spots and global trends in Takaful research through bibliometric analysis based on Scopus database (2001- 2022), Journal of Islamic Accounting and Business Research, 15(2), 291- 305. https://doi.org/10.1108/JIABR-02-2022-0055 Roriz, R., ve Pereira, J. L. (2019). Avoiding insurance fraud: A blockchain-based solution for the vehicle sector. Procedia Computer Science, 164, 211-218. https://doi.org/10.1016/j.procs.2019.12.174 SİSEB (Sigorta Sahteciliklerini Engelleme Bürosu) (2024). Sigorta Suistimalleri Bilgi Sistemi (SİSBİS) İstatistikleri. Erişim adresi https://siseb.sbm.org.tr/tr/istatistikler Sökmen, S., Dogan, M., ve Atabay, E. (2023). Turizm araştırmalarında nedensellik: Q1 dergilerde deneysel tasarımın kullanımına yönelik bir inceleme. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 23(3), 657-684. https://doi.org/10.18037/ausbd.1240722 Subudhi, S., ve Panigrahi, S. (2020). Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. Journal of King Saud University-Computer and Information Sciences, 32(5), 568-575. https://doi.org/10.1016/j.jksuci.2017.09.010 TSB (Türkiye Sigorta Birliği) (2023). Sektör Raporu 2022. Erişim adresi https://www.tsb.org.tr/content/Broadcasts/TSB_Sektör_Raporu_2022.pdf Verma, A., Taneja, A., ve Arora, A. (2017). Fraud detection and frequent pattern matching in insurance claims using data mining techniques. 2017 Tenth İnternational Conference on Contemporary Computing (IC3) içinde (1-7 ss.). IEEE. https://doi.org/10.1109/IC3.2017.8284299 Viaene, S., ve Dedene, G. (2004). Insurance fraud: Issues and challenges. The Geneva Papers on Risk and Insurance-Issues and Practice, 29, 313-333. https://doi.org/10.1111/j.1468-0440.2004.00290.x Wang, Y., ve Xu, W. (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105, 87-95. https://doi.org/10.1016/j.dss.2017.11.001 Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., ve Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of Medical Systems, 36, 2431-2448. https://doi.org/10.1007/s10916-011-9710-5 Zhang, R., Cheng, D., Yang, J., Ouyang, Y., Wu, X., Zheng, Y., ve Jiang, C. (2024). Pre-trained online contrastive learning for insurance fraud detection. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22511-22519. https://doi.org/10.1609/aaai.v38i20.30259