Journal of Quality Engineering and Management

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

Document Type : Original Article

Authors

Farbod Khanizadeh 1
Maryam Esna-Ashari 1
Farzan Khamesian 2
Azadeh Bahador 3

1 Faculty - Property and Casualty Insurance Research Group, Insurance Research center,Tehran,Iran

2 Faculty - Insurance macro studies Research Group, Insurance Research center,Tehran,Iran

3 Director of Auto Insurance Desk- Insurance research center- Tehran-Iran

https://doi.org/10.48313/jqem.2022.155152
Abstract
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.

Keywords

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.
Journal of Quality Engineering and Management
Volume 11, Issue 4
Winter 2022
Pages 413-428

Home

Editorial Board

Indexing and Abstracting

Guide for Authors

Submit Manuscript

Reviewers

Contact Us