A TYPICAL STUDY OF IMPROVING ACCURACY IN DETECTING INSURANCE FRAUD ON UNSTRUCTURED DATA SETS

Abstract

 Fraud in insurance health care brings significant financial and personal loss on individuals, business, government and society as a whole. The size of health care sector and the enormous volume of money involved make it an important fraud target. The big data trend, (the growth in unstructured data) always leaves lots of rooms for a fraud going undetected if data is not analyzed properly. Performing big data analysis can identify repetitive errors that are hidden and prevent the occurrence of them in future. The primary objective of this paper is to define existing challenges of fraud detection for the different types of large data sets and ways to extract the features that cause fraud. It also deals with the methods for improving accuracy by considering both true positives and true negatives, thereby performing data analytics.

Authors and Affiliations

N. Pratheeba

Keywords

Related Articles

 CPW ULTRA-WIDEBAND BANDPASS FILTER USING DEFECTED GROUND

 In this work, we are trying to develop a new filter that have good in-band and out-band and wideband property. The main part of this filter is a parallel-coupled microstrip-coplanar waveguide (CPW),an rectangular...

 SOLAR PANEL MODELING AN ANALYSIS WITH DIFFERENT TEMPERATURE AND SOLAR IRRADIATION POWER

 Solar panel is becomes an emerging topic foe the renewable energy world. The effective utilization of the solar panel and the constant power for small system to big energy system is required. The simulation and mo...

 EXTRACTION OF STANDING HUMAN BODIES FROM IMAGES WITH MULTILEVEL SEGMENTATION AND SPLINE REGRESSION

 The ability to extract and detection of human activities by computer vision is very important with many potentialapplications. Extraction of human bodies from images from respective digital image has accomplishedco...

 Robust Control of Uncertain Multiple T-S Fuzzy Neutral Systems with TimeVarying Delays

 This paper deals with the problem of robust control for a class of uncertain multiple Takagi-Sugeno fuzzy neutral systems with time-varying delays.A fuzzy filter is constructed,which ensures the robust stability.L...

 Cloud Databases: Future of Distributed Databases

 Hardware failures in current data centers are very frequently because of high volume data scales supported. Data replication is the better option for this condition. Distributed database is a concept of distributi...

Download PDF file
  • EP ID EP111990
  • DOI -
  • Views 75
  • Downloads 0

How To Cite

N. Pratheeba (2015).  A TYPICAL STUDY OF IMPROVING ACCURACY IN DETECTING INSURANCE FRAUD ON UNSTRUCTURED DATA SETS. International Journal of Engineering Sciences & Research Technology, 4(12), 330-335. https://europub.co.uk/articles/-A-111990