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Securitization of Mortgage Backed Securities using Data Mining Techniques


Author : Viraj Gada, Sumeet Deshpande and Apoorva Dhakras

Pages : 2504-2509
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Abstract

This paper describes different data mining techniques used in securitization of Mortgage Backed Securities. Mortgage Default Risk Analysis is used in many financial institutes for accurate analysis of consumer data to find defaulter and valid customer. For this different data mining techniques can be used. The information thus obtained can be used for Decision making. In this paper we study about Mortgage Default Risk Analysis, factors considered before sanctioning a mortgage and different data mining techniques like C4.5 algorithm, Support Vector Machines (SVM), Apriori algorithm, Adaboost algorithm, Naïve Bayes Classifier and K-means algorithm.

Keywords: Data Mining, Mortgage Default Risk Analysis, C4.5, Support Vector Machine, Apriori, Adaboost, Naïve Bayes, K-means algorithm

Article published in International Journal of Current Engineering and Technology, Vol.5, No.4 (Aug-2015)

 

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