Real-time Insurance Fraud Detection using Reinforcement Learning

• Published in: 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) pp. 1 - 6
• Main Authors: Saddi, Venkata Ramana, Boddu, Swetha, Gnanapa, Bhagawan, Gupta, Ketan, Kiruthiga, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23-02-2024
• Subjects: approach; environments; Fraud; fraudulent; Industries; Insurance; Real-time systems; Reinforcement learning; Reviews; simultaneously; Social networking (online); traditional
• DOI: 10.1109/ICICACS60521.2024.10498779

Reinforcement gaining knowledge of (RL) can revolutionize the field of coverage fraud detection by supplying an extra dynamic and timely method than traditional strategies. RL works in a loop of consecutive processing steps, so it can adapt to changing environments wherein fraudulent sports arise and the dangers related to them in real-time. RL fashions analyze from beyond studies to make choices on how to excellent reply to fraud tries and adjust the variables used for detection. Such models can examine diverse outside records sources, which include information reviews, public records, and social media, to detect any shifts inside the detected fraudulent activities. Furthermore, RL gives insurers an additional layer of safety that they will only have had entry to after while relying entirely on traditional strategies. RL can hit upon fraudulent activities as quickly as they appear, simultaneously as conventional strategies cannot. As such, insurers can take immediate and effective movement to lessen losses by responding to every case most appropriately. All in all, RL gives high-quality opportunities to coverage businesses and policyholders in fraud detection. Overall, real-time insurance fraud detection using reinforcement learning is a game-changer for the insurance industry. It not only improves fraud detection capabilities but also reduces costs and losses caused by fraudulent activities. This technology is continuously evolving, and as it becomes more advanced, it will become even more effective in combating insurance fraud. Its potential to use past studies and records sources for a dynamic detection approach and its ability to quickly reply to any fraudulent interest makes it an ideal approach to fraud detection for coverage businesses.