Fraud Detection in Insurance: A Data-Driven Approach Using Machine Learning Techniques
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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.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology 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 of Science & Technology. 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 the Journal of Science & Technology.
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 of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.