Leveraging Machine Learning for Fraud Detection in Life Insurance: Anomaly Detection and Supervised Learning Approaches

Preetham Reddy Kaukuntla

doi:10.5281/zenodo.14769929

Leveraging Machine Learning for Fraud Detection in Life Insurance: Anomaly Detection and Supervised Learning Approaches

Author(s)	Preetham Reddy Kaukuntla
Country	United States
Abstract	In this paper, we examine how machine learning techniques can be applied to detect fraud in life insurance and focus on approaches to both supervised learning and anomaly detection. We show a comparison of the differences in performance between models on accuracy, precision, recall, and F1-score of Random Forest, Support Vector Machine, and Isolation Forest. Based on the result, the algorithm of Random Forest performs the best with better accuracy and reached excellent F1 score, thus likely to have great potential in processing large data sets of insurance claims. In addition, applying the anomaly detection technique also seems worthwhile once one has limited data for labeling. There are several obstacles, such as data imbalance, difficulty selecting features, and the need to model interpretability. Furthermore, model updates are required because fraudulent activities change with time. To address these issues, future studies should be conducted to discover new data handling techniques, develop feature engineering processes, and design systems for continuous learning. Machine learning is a game-changer in the life insurance industry because it may allow for better identification of fraudulent claims and increase the trust level of policyholders.
Keywords	Machine Learning, Fraud Detection, Life Insurance, Supervised Learning, Anomaly Detection, Random Forest, Support Vector Machine, Isolation Forest.
Field	Engineering
Published In	Volume 3, Issue 2, February 2022
Published On	2022-02-09
Cite This	Leveraging Machine Learning for Fraud Detection in Life Insurance: Anomaly Detection and Supervised Learning Approaches - Preetham Reddy Kaukuntla - IJLRP Volume 3, Issue 2, February 2022. DOI 10.5281/zenodo.14769929
DOI	https://doi.org/10.5281/zenodo.14769929
Short DOI	https://doi.org/g83kwp

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Leveraging Machine Learning for Fraud Detection in Life Insurance: Anomaly Detection and Supervised Learning Approaches

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