International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Application of Reinforcement Learning in Detecting Fraudulent Insurance Claims

  • Choi, Jung-Moon (Department of Research and Planning WISEiTECH) ;
  • Kim, Ji-Hyeok (Department of Research and Planning WISEiTECH) ;
  • Kim, Sung-Jun (Department of Bigdata-Content Convergence, Namseoul University)
  • Received : 2021.09.05
  • Published : 2021.09.30
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Abstract

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

Keywords

Acknowledgement

This work was supported by the ATC program of the Ministry of Trade, Industry and Energy (MOTIE) and Korea Evaluation Institute of Industrial Technology (KEIT) (Assignment No. 10077293-Intelligent Fraudulent Claim Detection System's Technology Development that Improves the Fraudulent Claim Detection Rate by Over 40% Through Early Prediction of Fraudulent Insurance Claims).

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