A Digital Innovation and Transformation in the Insurance Sector-Embracing emerging technologies

Abstract

Fraudulent insurance claims pose significant financial and operational risks to the insurance industry. Machine Learning (ML) offers a promising solution to enhance fraud detection accuracy and efficiency. This study investigated the adoption of ML in the insurance sector for detecting fraudulent claims, exploring benefits, challenges, and future directions. A hybrid unsupervised machine learning model was developed and validated using a synthetic dataset of 1,087,654 insurance claims. The hybrid algorithm integrates K-means clustering, XGBoost, Principal Component Analysis (PCA), and Local Outlier Factor (LOF) to detect both individual and collusive fraud patterns. The study employed a mixed-methods approach combining semi-structured interviews with insurance professionals and quantitative analysis. Key findings revealed significant improvements in fraud detection accuracy, efficient claim processing, and data-driven insights. However, integration complexities, data quality issues, and regulatory compliance challenges persist. This research highlights the potential of unsupervised hybrid ML in transforming fraud detection in the insurance sector while providing recommendations for industry stakeholders, policymakers, and researchers.