Fraud Detection in Insurance: A Data-Driven Approach Using Machine Learning Techniques
Keywords:
Fraud Detection, Insurance, Machine Learning, Anomaly Detection, Predictive Modeling, Network Analysis, Data-driven Approach, Financial Losses, Ethical Considerations, Claim RecordsAbstract
Fraudulent activities within the insurance sector pose significant challenges, impacting both insurers and policyholders. To combat this issue effectively, this paper proposes a data-driven approach utilizing machine learning techniques for fraud detection in insurance. By leveraging anomaly detection, predictive modeling, and network analysis, this research aims to enhance fraud detection accuracy while minimizing false positives. The study explores various datasets, including claim records, customer profiles, and historical fraud instances, to train and validate machine learning models. Through comprehensive experimentation and analysis, this paper demonstrates the efficacy of the proposed approach in identifying fraudulent behavior patterns and mitigating financial losses. Furthermore, the research discusses the implementation challenges and ethical considerations associated with deploying machine learning-based fraud detection systems in the insurance industry. Overall, this paper contributes to the advancement of fraud detection methodologies in insurance through the integration of innovative data-driven techniques.
Readership Data
Refreshing Cached Analytics Data
The cached analytics data has become stale and www.thesciencebrigade.com is making a fresh request to fetch the latest data from Google Analytics. This may take 20-30 seconds depending on the server response time from Google Analytics. Please do not close the browser during this time. We appreciate your patience.
Downloads
References
Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.
Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
Friedman, Jerome H. "Stochastic gradient boosting." Computational Statistics & Data Analysis 38.4 (2002): 367-378.
Hand, David J., et al. "A statistical approach to credit scoring." Journal of the Royal Statistical Society: Series A (Statistics in Society) 160.3 (1997): 523-541.
Hastie, Trevor, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009.
Hawkins, Douglas M., et al. "Identification of fraud from unsolicited E-mail communications using self-organizing maps." Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1999.
Japkowicz, Nathalie. "The class imbalance problem: Significance and strategies." Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI). 2000.
Li, Kai Ming, and Paul M. Azzi. "Data mining techniques." Data Mining and Knowledge Discovery in Databases. Springer, 2005.
Liu, Bing. "Web data mining: Exploring hyperlinks, contents, and usage data." Data Mining and Knowledge Discovery 7.1 (2003): 5-22.
Michie, Donald, et al. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.
Mitchell, Tom M. Machine Learning. McGraw Hill, 1997.
Rish, Irina. "An empirical study of the naive Bayes classifier." IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence. 2001.
Shafer, John, et al. "Tutorial on detection of fraudulent telephone calls." Computing Science and Statistics 29 (1997): 397-405.
Smyth, Padhraic. "Modeling the distribution of normal data in preprocessed financial data streams." Proceedings of the Third IEEE International Conference on Data Mining. 2003.
Srivastava, Nitin, et al. "Web usage mining: Discovery and applications of usage patterns from web data." SIGKDD Explorations 1.2 (2000): 12-23.
Tan, Pang-Ning, et al. Introduction to Data Mining. Pearson, 2006.
Wang, Hongwei, et al. "A survey of data mining and knowledge discovery process models and methodologies." Knowledge and Information Systems 18.2 (2009): 181-211.
Witten, Ian H., and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
Wu, Xindong, et al. "Data mining with big data." IEEE Transactions on Knowledge and Data Engineering 26.1 (2014): 97-107.
Zhang, Zhongfei, and Jelena Tesic. "Analysis of credit card fraud detection techniques: A survey." Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. 2009.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
