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.
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