A secured predictive analytics using genetic algorithm and evolution strategies

Title

A secured predictive analytics using genetic algorithm and evolution strategies

Creator

Krishna A.V.N.; Pandey S.; Sarda R.

Description

In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates. 2020 by IGI Global. All rights reserved.

Source

Handbook of Research on Intelligent Data Processing and Information Security Systems, pp. 199-226.

Date

2019-01-01

Publisher

IGI Global

Coverage

Krishna A.V.N., Christ University, India; Pandey S., Christ University, India; Sarda R., Christ University, India

Rights

Restricted Access

Relation

ISBN: 978-179981292-0; 1799812901; 978-179981290-6

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/978-1-7998-1290-6.ch009

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