Abstract
Healthcare insurance fraud has become a major problem worldwide in recent decades, resulting in significant financial losses for every affected country. Traditional fraud detection methods, however, often fall short as they primarily focus on analyzing data from the current period, thereby neglecting valuable historical information. In our study, we introduce a novel approach inspired by the financial concept of “credit” to detect fraudulent activities in various domains, such as healthcare insurance, credit card, and online retail transactions. Our approach aims to build a credit evaluation model (CEM) that can distinguish between fraudulent and normal activities by analyzing their historical records. We acknowledge that numerous fraud detection methods have been proposed, but they often struggle to detect edge cases, which limits their practical effectiveness. To address this challenge, our proposed CEM employs a time interval-aware long short-term memory (LSTM) algorithm to assist fraud detection. Furthermore, we propose an innovative approach that transforms traditional binary classification into a multi-classification problem, which improves the model’s ability to handle diverse fraudulent activities. We conducted experiments to evaluate the effectiveness of our proposed approach and model, comparing them against baseline algorithms and recently proposed methods. The results indicate that our approach outperforms the others, demonstrating its potential for practical use in detecting fraudulent activities across various domains.
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