Securitization of Mortgage Backed Securities using Data Mining Techniques

Authors

  • Viraj Gada Computer Engineering, Mumbai University, Dadar, Mumbai, India Author
  • Sumeet Deshpande Computer Engineering, Mumbai University, Shivaji Park, Mumbai, India Author
  • Apoorva Dhakras Computer Engineering, Mumbai University, Andheri, Mumbai, India Author

DOI:

https://doi.org/10.14741/

Keywords:

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

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

References

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Published

2015-08-31

Issue

Vol.5, No.4 (Jul/Aug 2015)

Section

Articles

How to Cite

Securitization of Mortgage Backed Securities using Data Mining Techniques. (2015). International Journal of Current Engineering and Technology, 5(4), 2504-2509. https://doi.org/10.14741/