文献类型： 会议论文
第一作者： Bediga Sharan
作者： Bediga Sharan 1 ; Mohammad Hassan 2 ; V Divya Vani 3 ; Vijilius Helena Raj 4 ; GinniNijhawan 5 ; Priyanka Prabhakar Pawar 6 ;
作者机构： 1.School of CS & AI, SR University, Warangal, Telangana, India
2.Founder, Sagelance, Dhaka, Bangladesh
3.Institute of Aeronautical Engineering, Dundigal, Hyderabad
4.Department of Applied Sciences, New Horizon College of Engineering, Bangalore
5.Lovely Professional University, Phagwara
6.Dr. D. Y. Patil, School of Science and Technology, Dr. D. Y. Patil, Vidyapeeth (Deemed to be University), Pimpri, Pune, Maharashtra, India
关键词： Industries;Machine learning algorithms;Insurance;Machine learning;Market research;Real-time systems;Hazards
会议名称： International Conference on Advances in Computing, Communication and Applied Informatics
主办单位：
页码： 1-5
摘要： The insurance industry is becoming increasingly concerned about the possibility of fraudulent insurance claims as it uses Internet of Things (IoT) technologies to improve customer service and expedite procedures. In this context, a viable method to improve fraud detection capabilities in IoT-enabled insurance systems is the incorporation of machine learning (ML) algorithms. This study suggests a fraud-detecting approach based on machine literacy that is tailored for insurance claims in an Internet of Things environment. The suggested solution makes use of real-time data from IoT detectors and actual claim records, applying machine learning techniques like anomaly finding, bracketing, and clustering to spot suspicious trends and flag possibly fraudulent claims. The efficacy and efficiency of the suggested method are proven through a thorough examination utilizing deconstructed and real-world datasets, underscoring its possibility to reduce fraud hazards and improve the integrity of insurance operations in IoT environments.
分类号： tp3
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Fraud Detection in IoT-Based Financial Transactions Using Anomaly Detection Techniques
作者：Kafila;Mohammad Hassan;Ch Veena;Atul Singla;Amit Joshi;Melanie Lourens
关键词：Industries;Prevention and mitigation;Bones;Hazards;Fraud;Internet of Things;Security
