Insurance Fraud Detection Using Big Data Analytics
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 9
Abstract
The volume of data available to businesses from both internal systems and external channels has led to a new category of application known as “Big Data”. For insurers, the benefits of using analytical applications that tap into the Big Data stream are significant. These applications can provide information to enhance sales, marketing, operational activities that reduce costs and strategies to better understand and reduce risk. Handling fraud manually has always been costly for insurance companies, even if one or two low risk incidences of high value fraud went undetected. In addition to this, the growth in unstructured data always leaves lot of room for fraud going undetected, if data is not analyzed thoroughly. Traditionally, insurance companies use statistical models to identify fraudulent claims. Big Data Analytics addresses the big data challenges and plays a crucial role in fraud detection for insurance companies. Big Data applications are providing a faster, easier solution for insurers. Hadoop is an excellent open source framework for Big data analytics. The objective of the project is to develop a risk assessment model using Big Data Analytics for insurance fraud detection implemented in hadoop framework that analyzes and processes large volume of customer reference data that are dynamic, diverse in formats and change frequently.
Authors and Affiliations
A. Saran Kumar, V. Vanitha Devi
Keywords
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