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An efficient approach for clustering and classification for fraud detection using bankruptcy data in iot environment

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International Journal of Information Technology (Singapore) 2021年第6期13卷 2497-2503页

作者： Srikanth, Panigrahi Department of CSE GMR Institute of Technology GMR Nagar Rajam 532127 Andhra Pradesh India

The fraud identification area is distinguished by a classify the data (also known as fraud/legitimate) which are not representative general of the population due to sampling bias. To deal with unsupervised fraud detection (FD) methods, which are uncommon in policies fraud detection studies, we use with these problems with the domain. Our approach consists of applying FD approaches to companies in the same sector and facilitates an effective auditing technique that can be implemented by policy authorities. Therefore, new technology must be researched and evaluated in order to leverage the insight gained from the study for the prediction of fraud and for the creation of systems to avoid fraud with additional checks for minimizing suspicious occurrences. Increased reliance on emerging technology such as Internet of Things (iot) with FD has compounded the issue in recent years. In this context, the key challenges are to determine the proposed novel similarity measure to satisfy properties, to generate clusters with the proposed measure of similarity and to minimize dimensionality, and to recognize fraud to increase machine efficiency. Our administration involves the regulation issuers to effectively identify illegal activities to protect consumer rights and reduce fraud risks and compliance costs. Our proposed methods are highly efficient and successful have demonstrated real time polish companies data to improve high accuracy and system performance. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Bankruptcy data Classification algorithm Clustering algorithm Dimensionality reduction fraud detection with iot Similarity measure

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