Exploratory analysis on prediction of loan privilege for customers using random forest

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.



  • Keywords

    Machine learning, random forest, prediction, R.

  • References

      [1] Sudhamathy G & Venkateswaran J, “Analytics Using R for Predicting Credit Defaulters”, IEEE international conference on advances in computer applications, (2016).

      [2] Jina Y & Zhua Y, “A data-driven approach to predict default risk of loan for online Peer-to-Peer(P2P) lending”, Fifth International Conference on Communication Systems and Network Technologies, (2015).

      [3] Odeh O, Koduru P, Featherstone A, Das S & Welch SM, “A multi-objective approach for the prediction of loan defaults”, Elsevier/ Expert Systems with Applications, Vol.38, (2011), pp.8850–8857

      [4] Aboobyda JH & Tarig MA, “Developing Prediction Model Of Loan Risk In Banks Using Data Mining”, Machine Learning and Applications: An International Journal (MLAIJ), Vol.3, No1, (2016), pp. 1–9.

      [5] Tsai MC, Lin SP, Cheng CC & YP Lin, “The consumer loan default predicting model–An application of DEA–DA and neural network”, Elsevier Expert Systems with Applications, Vol.36, (2009), pp.11682–11690




Article ID: 12399
DOI: 10.14419/ijet.v7i2.21.12399

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.