AN IMPROVISED MACHINE LEARNING TECHNIQUES FOR ANOMALY DETECTION

Insurance fraud detection is a challenging problem due to the diversity of fraudulent schemes and the lack of known fraud cases in typical datasets.Developing effective detection models necessitates a balance between minimizing financial losses from fraud and controlling the costs associated with anomalies.This paper addresses the complexities of insurance fraud detection by employing machine learning techniques for anomaly detection namely DBSCAN, Autoencoders, and Isolation Forest.Machine learning, with its ability to learn from data and improve over time, provides significant tools for detecting fraudulent patterns that older methods may not reveal.Effective fraud detection algorithms must find a balance between avoiding financial losses and minimizing the costs associated with anomalies.Anomaly detection, a crucial aspect of machine learning, identifies data points that significantly deviate from the norm within a dataset.Detecting anomalies is crucial for maintaining data integrity and ensuring accurate analysis and decision-making, as anomalies can indicate errors, rare events, or fraudulent activities.This research also explores the enhancement of anomaly detection through the ensemble of various machine-learning techniques for anomaly detection.The ensemble approach combines the strengths of different algorithms to improve overall detection accuracy.In this study, an ensemble-based machine-learning techniques has been proposed for anomaly detection using evaluation parameters such as accuracy, precision, recall, and F1-score.The findings demonstrate that the ensemble approach offers superior anomaly detection capabilities, providing a robust solution for insurance fraud detection.