What is it about?
In this paper we have analyzed the huge volume of warranty data for segregating the fraudulent warranty claims using pattern recognition and clustering methodology. Recent survey of automotive industry shows up to 10% of warranty costs are related to warranty claims fraud, costing manufacturers several billions of dollars. Most of the automotive companies are suspecting and aware of warranty fraud. But they are not sure of the extent and ways to eliminate it. The existing methods to detect warranty fraud are very complex and expensive as they are dealing with inaccurate and vague data, causing manufacturers to bear the excessive costs. We are proposing model to find anomalies on warranty data along with component failure data and patterns based on historic warranty claims data under particular region and for specific component as the data are of high volume.
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Why is it important?
Useful in optimizing the warranty analysis.
Perspectives
Different modelling approach for warranty analysis.
Dr N Ethiraj
Dr.M.G.R Educational and Research Institute
Read the Original
This page is a summary of: Modelling an Optimized Warranty Analysis methodology for fleet industry using data mining clustering methodologies with Fraud detection mechanism using pattern recognition on hybrid analytic approach, Procedia Computer Science, January 2016, Elsevier,
DOI: 10.1016/j.procs.2016.06.001.
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