متغیر جایگزین، رویکردی نوین در جهت افزایش کیفیت کشف تقلبات بیمه‌های ‌اتومبیل با استفاده از الگوریتم‌های ‌با نظارت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 هیات علمی- گروه پژوهشی بیمه های اموال و مسئولیت- پژوهشکده بیمه- تهران- ایران

2 هیات علمی- گروه پژوهشی بیمه‌های اموال و مسئولیت، پژوهشکده بیمه، تهران، ایران

3 هیات علمی- گروه مطالعات کلان بیمه- پژوهشکده بیمه-تهران- ایران

4 راهبر میز تخصصی بیمه های اتومبیل

چکیده

در سال‌های ‌اخیر تمایل صنعت بیمه به تجهیز شرکت‌ها به سیستم‌های ‌کشف تقلب افزایش یافته است. با توجه به هزینه زیادی که اینگونه پرونده‌ها به صنعت وارد می‌‌‌‌کند، الگوریتم‌های ‌کشف و شناسایی تقلب می‌‌‌‌بایست بخش جدایی‌ناپذیری از شرکت‌های بیمه باشند. لیکن مشکل اساسی، کیفیت خروجی سیستم‌های ‌کشف تقلب است. از طرفی الگوریتم‌های ‌با نظارت نسبت به الگوریتم‌های ‌بدون نظارت دقت بالاتری دارند. از طرف دیگر در حوزه کشف تقلب، داده‌های ‌برچسب‌گذاری شده محدودند و بنابراین به‌کارگیری الگوریتم‌های ‌با نظارت و دقت و کیفیت آنها با چالش مواجه می‌‌‌‌شود. در این مقاله برای رفع این چالش، با استفاده از رویکرد "متغیر جایگزین"، از متغیر دیگری که مقادیر آن در دسترس بوده و شاخص مناسبی برای پرونده‌های ‌مشکوک می‌‌‌‌باشد استفاده شده است. این رویکرد، باعث بهبود کارایی و کیفیت سیستم شده و به شرکت‌های ‌بیمه این امکان را می‌‌‌‌دهد که با اطمینان بیشتر و خطای کمتر نسبت به پرونده‌های ‌مشکوک اقدام کنند.

کلیدواژه‌ها


عنوان مقاله [English]

Target replacement , a new approach to increase the performance of fraud detection system in auto insurance utilizing supervising learning

نویسندگان [English]

  • Farbod Khanizadeh 1
  • Maryam Esna-Ashari 2
  • Farzan Khamesian 3
  • Azadeh Bahador 4
1 Faculty - Property and Casualty Insurance Research Group, Insurance Research center,Tehran,Iran
2 Faculty - Property and Casualty Insurance Research Group, Insurance Research center,Tehran,Iran
3 Faculty - Insurance macro studies Research Group, Insurance Research center,Tehran,Iran
4 Director of Auto Insurance Desk- Insurance research center- Tehran-Iran
چکیده [English]

Recent years, the insurance industry has been experiencing an increase in equipping insurance companies with fraud detection systems. Furthermore due to the significant cost imposed on the insurance industry by the rise in such claims, the role of data mining techniques in detecting fraudulent claims has become widespread. However an essential issue with such systems is the quality of their outputs. On one hand, supervised algorithms are more accurate comparing to unsupervised counterparts. On the other hand, as data labeled fraud is really limited, the efficiency of supervised algorithms is severely challenged. Within this regard, a novel approach is introduced as “alternative feature” to overcome the challenge. Basically, alternative feature is a variable whose values are available and can be considered a suitable indicator to detect suspicious cases. This approach improves the efficiency of the system and allows experts and insurance companies to investigate suspicious cases with more confidence and less error.

کلیدواژه‌ها [English]

  • supervised learning
  • target replacement
  • fraud detection
  • auto insurance
  • Agaskar, V., Babariya, M., Chandran, S., & Giri, N. (2017). Unsupervised learning for credit card fraud detection. International Research Journal of Engineering and Technology (IRJET), 4(3), 2343-2346.
  • Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.
  • Bolton, R. J., & Hand, D. J. (2001). Unsupervised profiling methods for fraud detection. Credit scoring and credit control VII, 235-255.
  • Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical science, 17(3), 235-255.
  • Brockett, P. L., Derrig, R. A., Golden, L. L., Levine, A., & Alpert, M. (2002). Fraud classification using principal component analysis of RIDITs. Journal of Risk and insurance, 69(3), 341-371.
  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
  • Chen, J. L., & Lai, K. L. (2021). Deep convolution neural network model for credit-card fraud detection and alert. J. Artif. Intell., 3(02), 101-112.
  • Derrig, R. A. (2002). Insurance fraud. Journal of Risk and Insurance, 69(3), 271-287.
  • Dheepa, V., & Dhanapal, R. (2012). Behavior based credit card fraud detection using support vector machines. ICTACT Journal on Soft computing, 2(4), 391-397.
  • Domingues, R. (2015). Machine Learning for Unsupervised Fraud Detection.
  • Gomes, C., Jin, Z., & Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88(3), 591-624.
  • Gyamfi, N. K., & Abdulai, J. D. (2018, November). Bank fraud detection using support vector machine. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 37-41). IEEE.
  • Lepoivre, M. R., Avanzini, C. O., Bignon, G., Legendre, L., & Piwele, A. K. (2016). Credit card fraud detection with unsupervised algorithms. Journal of advances in information technology, 7(1).
  • Li, J., Huang, K. Y., Jin, J., & Shi, J. (2008). A survey on statistical methods for health care fraud detection. Health care management science, 11(3), 275-287.
  • Nian, K., Zhang, H., Tayal, A., Coleman, T., & Li, Y. (2016). Auto insurance fraud detection using unsupervised spectral ranking for anomaly. The Journal of Finance and Data Science, 2(1), 58-75.
  • Noble, C. C., Cook, D. J. (2003, August). Graph-based anomaly detection. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 631-636).
  • Peng, Y., Kou, G., Sabatka, A., Chen, Z., Khazanchi, D., & Shi, Y. (2006, October). Application of clustering methods to health insurance fraud detection. In 2006 International Conference on Service Systems and Service Management (Vol. 1, pp. 116-120). IEEE.
  • Phua, C., Alahakoon, D., & Lee, V. (2004). Minority report in fraud detection: classification of skewed data. Acm sigkdd explorations newsletter, 6(1), 50-59.
  • Rushin, G., Stancil, C., Sun, M., Adams, S., & Beling, P. (2017, April). Horse race analysis in credit card fraud—deep learning, logistic regression, and Gradient Boosted Tree. In 2017 systems and information engineering design symposium (SIEDS) (pp. 117-121). IEEE.
  • Sabau, A. S. (2012). Survey of clustering based financial fraud detection research. Informatica Economica, 16(1), 110.
  • Sahin, Y., Bulkan, S., & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 40(15), 5916-5923.
  • Sahin, Y., Duman, E. (2011, June). Detecting credit card fraud by ANN and logistic regression. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 315-319). IEEE.
  • Subudhi, S., Panigrahi, S. (2018). Detection of automobile insurance fraud using feature selection and data mining techniques. International Journal of Rough Sets and Data Analysis, 5(3), 1-20.
  • Viaene, S., Ayuso, M., Guillen, M., Van Gheel, D., & Dedene, G. (2007). Strategies for detecting fraudulent claims in the automobile insurance industry. European Journal of Operational Research, 176(1), 565-583.
  • Viaene, S., Dedene, G. (2004). Insurance fraud: issues and challenges. The Geneva Papers on Risk and Insurance-Issues and Practice, 29(2), 313-333.
  • Viaene, S., Derrig, R. A., Baesens, B., & Dedene, G. (2002). A comparison of state‐of‐the‐art classification techniques for expert automobile insurance claim fraud detection. Journal of Risk and Insurance, 69(3), 373-421.
  • Yaram, S. (2016, August). Machine learning algorithms for document clustering and fraud detection. In 2016 International Conference on Data Science and Engineering (ICDSE) (pp. 1-6). IEEE.
  • Yuan, S., Wu, X., Li, J., & Lu, A. (2017, November). Spectrum-based deep neural networks for fraud detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 2419-2422).
  • Zou, K., Sun, W., Yu, H., & Liu, F. (2012, March). ID3 decision tree in fraud detection application. In 2012 International Conference on Computer Science and Electronics Engineering (Vol. 3, pp. 399-402). IEEE.