Deep Learning in Insurance Fraud Detection: Techniques, Datasets, and Emerging Trends

Abstract

Insurance fraud represents a significant financial burden globally, with annual losses exceeding $200 billion across healthcare, auto, and life insurance sectors. Traditional rule-based fraud detection systems have proven inadequate against increasingly sophisticated fraudulent schemes, prompting widespread adoption of deep learning (DL) approaches. This comprehensive review systematically examines the application of DL techniques to insurance fraud detection, analyzing 57 peer-reviewed studies published between 2019 and 2025. We evaluate the effectiveness of various architectures including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Graph Neural Networks (GNNs), and hybrid models across healthcare, auto, and life insurance domains. Our analysis reveals that ensemble methods combining CNNs with LSTMs achieve accuracies ranging from 89.6% to 98%, while GNN-based approaches demonstrate superior performance in detecting collusive fraud networks with accuracies exceeding 84%. The review identifies critical challenges including severe class imbalance with fraud rates of 0.03-3%, model interpretability requirements, and limited availability of labeled datasets. We examine emerging trends including explainable artificial intelligence (XAI) frameworks, attention mechanisms, generative adversarial networks (GANs) for synthetic data generation, and federated learning approaches for privacy-preserving fraud detection. This review contributes to understanding the current state-of-the-art in DL for insurance fraud detection while highlighting critical research gaps and future directions in model interpretability, cross-domain transfer learning, and real-time detection systems.