A Comprehensive Survey of Recent Advances Artificial Intelligence for Insurance Fraud Detection
TL;DR
Data quality, class imbalance, privacy preservation, model interpretability, scalability and regulatory compliance are among the key challenges that are critically analyzed.
Abstract
Insurance fraud is a big problem that the insurance industry is trying to solve. Economic loss, operational costs, and a decline in consumer trust are all consequences of insurance fraud. Traditional techniques of fraud detection, such as rule-based systems and human processes, frequently fail to detect more sophisticated fraud schemes. By analyzing complicated data in real-time, artificial intelligence (AI) has been shown to be an effective tool for automating the identification of fraud. Machine learning, explainable AI, federated learning, deep learning, reinforcement learning, natural language processing, and the most current approaches to AI methods in insurance fraud detection are included in this review. The paper also contains a discussion on traditional fraud detection methods, most popular insurance frauds, data preprocessing methods and some common metrics. Additionally, it looks at the latest research studies and points out the advantages and disadvantages of the available AI-driven approaches. Data quality, class imbalance, privacy preservation, model interpretability, scalability and regulatory compliance are among the key challenges that are critically analyzed.