Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

A Implementation on Predictive Analytics for Banking Issues using Mining Technique Algorithms

Author : Shahina Farheen 1 Manika Manerkar 2 Trupti Payal 3 Dr. K. Vikram 4

Date of Publication :21st March 2018

Abstract: Nowadays, there are many risks related to bank loans, health loan, car loan, for the bank and for those who get the loans. The analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of transactions in banking sector is rapidly growing and huge data volumes are available which represent the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating and vital area of research with the aim of extracting information from tremendous amount of accumulated data sets. In this paper a new model for classifying loan in banking sector by using data mining. The model has been built using data form banking sector to predict the status of loans particular user if they want. Here we find out the interested user who are want the service form the banking only those user meet them and discuss them without wasting time, money, man power

Reference :

    1. Pulakkazhy, Sreekumar, and R. V. S. Balan. "Data mining in banking and its applications-a review." Journal of computer science 9.10 (2013): 1252.
    2. Chitra, K., and B. Subashini. "Data Mining Techniques and its Applications in Banking Sector." International Journal of Emerging Technology and Advanced Engineering 3.8 (2013): 219-226.
    3. Zurada, Jozef, and Martin Zurada. "How Secure Are “Good Loans”: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans."Review of Business Information Systems (RBIS) 6.3 (2011): 65-84.
    4.  Strahan, Philip E. "Borrower risk and the price and nonprice terms of bank loans." FRB of New York Staff Report 90 (1999). [5] Tomar, Divya, and Sonali Agarwal. "A survey on Data Mining approaches for Healthcare." International Journal of Bio-Science and Bio-Technology 5.5 (2013): 241-266.
    5. Tomar, Divya, and Sonali Agarwal. "A survey on Data Mining approaches for Healthcare." International Journal of Bio-Science and Bio-Technology 5.5 (2013): 241-266.
    6. Sharma, Poonam, and Gudla Balakrishna. "PrefixSpan: Mining Sequential Patterns by PrefixProjected Pattern." International Journal of Computer Science and Engineering Survey 2.4 (2011): 111.
    7. O'guinn, Thomas.” Advertising and Integrated Brand Promotion”. Oxford Oxfordshire: Oxford University Press. p. 625. ISBN 978-0-324-56862-2. , 2008.
    8. Ou, C., Liu, C., Huang, J. and Zhong, N. „One Data mining for direct marketing‟, Springer-Verlag Berlin Heidelberg, pp. 491–498., 2003.
    9. Petrison, L. A., Blattberg, R. C. and Wang, P. „Database marketing: Past present, and future‟, Journal of Direct Marketing, 11, 4, 109–125, 1997.
    10. T. Munkata, “Fundamentals of new artificial intelligence,” 2nd edition, London, Springer-Verlag, 2008.

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